∙ IEEE Conference on Computer Vision and Pattern Recognition for a given dataset, defined as the minimum resolution yielding an IoU not statistically significantly different from the maximum IoU across all resolutions within each dataset. In general, if you want to classify an image into a certain category, you use image classification. RGB Salient object detection is a task-based on a visual attention mechanism, in which algorithms aim to explore objects or regions more attentive than the surrounding areas on the scene or RGB images. The University of Sydney (B. Leibe, J. Matas, However, it is difficult to obtain a domain-invariant detector when there is large discrepancy between different domains. Inspired by this mechanism’s speed and efficiency, many attempts have leveraged saliency-based models to generate object-only region proposals in object detection [13, 14, 15, 16, 17]. 20 hardcover $49.95.,” in, V. H. Perry, R. Oehler, and A. Cowey, “Retinal ganglion cells that project to State-of-the-art deep learning models, such as Faster R-CNN [2], YOLO [3], and SSD [4], achieve unprecedented object detection accuracy at the expense of high computational costs [5]. classification of typically 10^4-10^5 regions per image. The Python Keras API with the TensorFlow framework backend was used to implement and train each model on the respective subset training images end-to-end and from scratch (. Deep learning object detectors achieve state-of-the-art accuracy at the expense of high computational overheads, impeding their utilization on embedded Secondly, an object can be simply defined as something that occupies a region of visual space and is distinguishable from its surroundings. IEEE Conference salient, while ignoring irrelevant stimuli such as background. There is, however, some overlap between these two scenarios. BLrI maps every label LrI into a binary image of the same resolution (Figure 5. After thoroughly and carefully researching the visual neuroscience literature, particularly on the superior colliculus, selective attention, and the retinocollicular visual pathway, we discovered new, overlooked knowledge that gave us new insights into the mechanisms underlying speed and efficiency in detecting objects in biological vision systems. The low-resolution grayscale (LG) compression of visual space performed by the retinocollicular pathway has multiple benefits. Please provide details on exactly how you have tried to solve the problem but failed. Attentional Object Detector Proposals Detector 28. viewing of natural dynamic video,” in, B. J. En masse, the studies by Perry and Cowey [18, 35], Veale [19], and White [21] summarize object detection in human and primate vision as follows: the retinocollicular pathway (dashed gray line in Figure 3) shrinks the high-resolution color image projected onto the retina from the visual field into a tiny colorless, e.g. attention allowed us to formulate a new hypothesis of object detection Evolutionarily, we can assume that visual regions and stimuli of interest moulded the retinocollicular pathway in a given species. Notes in Computer Science, pp. Therefore, it seems reasonable to hypothesize that the optimal retinocollicular compression resolution depends on the dataset. Abstract: The field of object detection has made great progress in recent years. -C). J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders, the dashed gray line and SC in Figure 3). (D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, eds. International Conference on Intelligent Transportation Systems Recognition,” pp. Our method rarely evaluates background regions, thus significantly reduces computational costs. rev 2021.1.21.38376, Stack Overflow works best with JavaScript enabled, Where developers & technologists share private knowledge with coworkers, Programming & related technical career opportunities, Recruit tech talent & build your employer brand, Reach developers & technologists worldwide. ∙ Therefore, for the purpose of training a binary classifier, we can treat all positive classes (Figure 5B) as the same class (Figure 5C) so that the classifier can generalize saliency across different object classes. 02/04/2020 ∙ by Hefei Ling, et al. share. 14 4x4 grid with no trominoes containing repeating colors. 11/24/2017 ∙ by Yao Zhai, et al. Detectors With Online Hard Example Mining,” in, Proceedings A significantly smaller achromatic portion is sent to the superior colliculus, where the saliency map is generated. Code for paper in CVPR2019, 'Shifting More Attention to Video Salient Object Detection', Deng-Ping Fan, Wenguan Wang, Ming-Ming Cheng, Jianbing Shen. ∙ Trade-Offs for Modern Convolutional Object Detectors,” in, 2017 IEEE Conference on Computer Vision and Pattern Recognition Given that most of share, Detecting objects in aerial images is challenging for at least two reaso... The remaining ∼10% comprises mainly achromatic Pγ, Pϵ, and (some) Pα, neurons that are relatively uniformly distributed throughout the retina and only project to the SC. Consequently, we discovered that salience detection is efficient because the retina compresses visual information from the entire visual field by ∼90% to a low-resolution achromatic image [18]. Does the double jeopardy clause prevent being charged again for the same crime or being charged again for the same action? These methods regard images as bags and object proposals as instances. We define roptimal. site design / logo © 2021 Stack Exchange Inc; user contributions licensed under cc by-sa. Sinauer Associates, Inc., Sunderland, MA, 1995. xvi + 476 pp., This study provides two main contributions: (1) unveiling the mechanism behind speed and efficiency in selective visual attention; and (2) establishing a new RPN based on this mechanism and demonstrating the significant cost reduction and dramatic speedup over state-of-the-art object detectors. Why hasn't Russia or China come up with any system yet to bypass USD? ∙ But with the recent advances in hardware and deep learning, this computer vision field has become a whole lot easier and more intuitive.Check out the below image as an example. (CVPR), A. Shrivastava, R. Sukthankar, J. Malik, and A. Gupta, “Beyond Skip your coworkers to find and share information. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for 770–778, 2016. and Pattern Recognition (CVPR), T.-Y. Small, Low Power Fully Convolutional Neural Networks for The brain then selectively attends to these regions serially to process them further e.g. Fortunately, two studies by Perry and Cowey in 1984 [18, 35] investigated the neural circuitry entering the SC from the eye via the retinocollicular pathway in the Macaque monkey, which has historically been a good representative animal model for studying primate and human vision. dataset. The training images were propagated through the neural network in batches of 64. ∙ The SC then aligns the fovea to attend to one of these regions, thereby sending higher-acuity, e.g. FLOPs gives us a platform-independent measure of computation, which may not necessarily be linear with inference time for a number of reasons, such as caching, I/O, and hardware optimization [40]. ), Lecture Notes in In their paper, the authors chose 64 pixels as the target low-resolution height since. Superior colliculus region proposal network (SC-RPN) architecture. Our detection mechanism with a single attention model does everything necessary for a detection pipeline but yields state-of-the-art performance. White, D. J. Berg, J. Y. Kan, R. A. Marino, L. Itti, and D. P. Munoz, share. 11/19/2018 ∙ by Shivanthan Yohanandan, et al. For evaluation purposes, we used the COCO 2017 dataset [38], which is a very popular benchmark for object detection, segmentation, and captioning. Object detection is a core computer vision task and there is a growing demand for enabling this capability on embedded devices. Finally, to determine (3) and (4), we needed to measure the SC-RPN’s computational costs and inference times across all 6 input resolutions. We observe that the SC-RPN is able to treat objects of different classes as the same salience class (fourth row in each subset). By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy. Remember, we are first interested in detecting the presence of an object; what its color or other feature-specific properties are seem only essential for classification. In this Object Detection Tutorial, we’ll focus on Deep Learning Object Detection as Tensorflow uses Deep Learning for computation. 04/18/2019 ∙ by Hei Law, et al. I am using Attention Model for detecting the object in the camera captured image. Therefore, high-resolution details about objects, such as texture, patterns, and shape, seem irrelevant and superfluous. Real-time Embedded Object Detection,” Feb. 2018. Remove moving objects to get the background model from multiple images, Retrain object detection model with own images (tensorflow). Vision-based object detection is one of the most active research areas in computer vision for a long time. Specifically, we learned that a midbrain structure known as the superior colliculus receives heavily-reduced achromatic visual information from the eye, which it then uses to compute a saliency map that highlights object-only regions for further cognitive analyses. We also learned that the degree of visual information reduction is species-dependent and consequently dependent on the visual environment; thereby, allowing us to think of object detection training datasets in a similar manner. In early years, object detec- tion was usually formulated as a sliding window classi・…a- tion problem using handcrafted features [14, 15, 16]. pp. We then performed two-tailed Student’s. This results in enormous efficiency, since it is reasonable to assume that more neurons would be required to represent the high-resolution details of a larger visual search space, resulting in higher computation and thus, energy demands. on Computer Vision and Pattern Recognition (CVPR), L. Duan, J. Gu, Z. Yang, J. Miao, W. Ma, and C. Wu, “Bio-inspired Visual Applications Of Object Detection … ), Therefore, computationally, we can think of objects and regions of interest in the visual environment as being our positive (salient) class, and everything else as background, which is analogous to a training dataset containing images with background and positively labelled object regions. Attention Window and Object Detection, Tracking, Labeling, and Video Captioning. Figure 8 complementarily echos the significant reduction in computational overheads by showing that the SC-RPN is capable of generating the complete set of region proposals at 500 frames/s. Our goal is to reduce computational costs associated with exhaustive region classification in object detection; hence, we are only interested in implementing and investigating the portion of the pipeline that generates the saliency map (i.e. Z. Wojna, Y. Hence, different input resolutions are studied in our experiments presented in Section 5. kernel max-pooling layers (red), to transform the image into multidimensional feature representations, before applying a stack of deconvolution layers (yellow) for upsampling the extracted coarse features. May 2019; DOI: 10.1109/ICASSP.2019.8682746. In particular, LrI maps every image into a grayscale image of the same resolution r where the k-object instances are highlighted against the background by assigning each class a unique grayscale value (Figure 5-B). share, Object detection is a fundamental task for robots to operate in unstruct... Do US presidential pardons include the cancellation of financial punishments? T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, Connections: Top-Down Modulation for Object Detection,” in, J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” in, H. Karaoguz and P. Jensfelt, “Fusing Saliency Maps with Region General Object Detection. Neural Information Processing Systems 25. Insights from behaviour, neurobiology and modelling,” in, B. J. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. A mean-squared error loss function was implemented to compute loss for gradient descent. The Mask Region-based Convolutional Neural Network, or Mask R-CNN, model is one of the state-of-the-art approaches for object recognition tasks. However, two recent papers by independent research teams [19, 21] converged on the claim that the saliency map is actually generated in a significantly smaller and more primitive structure called the superior colliculus (SC). colliculus encodes visual saliency before the primary visual cortex,” in, Proceedings of the National Academy of Sciences, L. Siklóssy and E. Tulp, “The space reduction method: a method to reduce the 1097–1105, Curran I have followed show-attend-and-tell (caption generation). Is cycling on this 35mph road too dangerous? Input images into these networks are typically re-scaled to. ), pp. A. Nevertheless, while two-stage detectors achieved unprecedented accuracies, they were slow. To determine (1), we needed a dataset with images containing multiple object category classes in order to assign all positive classes the same label, thus forming groundtruth saliency labels for each dataset. Most of the recent successful object detection methods are … In contrast, most salience-guided object detection models typically employed high-resolution (. These saliency-based approaches were inspired by the right idea; however, their implementations may not have been an accurate reflection of how saliency works in natural vision. To the authors’ knowledge, this is the first paper proposing a plausible hypothesis explaining how salience detection and selective attention in human and primate vision is fast and efficient. Some related work 29. One of the most typical solutions to maintain frame association is exploiting optical flow between consecutive frames. Small Object Detection using Context and Attention. However, in the case of humans, the attention mechanism, global structure information, and local details of objects all play an important role for detecting an object. low-resolution grayscale, image, which can then be scanned quickly by the SC to highlight peripheral regions worth attending to via the saliency map. With the rise of deep learning, CNN-based methods have become the dominant object detection solution. A. ), Lecture Notes in Computer Science, ∙ Intuitively, saliency-based approaches should be able to improve detection efficiency if implemented correctly. region detection,” in, Visual Communications and Image Processing However, with the resurgence of deep learning [23], two-stage detectors quickly came to dominate object detection. The sliding-window approach was the leading detection paradigm in classic object detection. A primary source of these overheads is the exhaustive A problem with this approach is that not all objects of interest are detected; just objects that grab human attention, which is inadequate for general object detection. The network takes an input image, adopts convolution layers (blue) with. Object detection is a computer technology related to computer vision and image processing which deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos On the other hand, if you aim to identify the location of objects in an image, and, for example, count the number of instances of an object, you can use object detection. To investigate (2), we needed to compare the SC-RPN’s accuracy on different image resolutions across contextually different datasets. of the IEEE Conference on Computer Vision and Pattern An NVIDIA Tesla K80 GPU was used for training and inference. Effective Detection Proposals?,” in, IEEE Transactions on Both one-stage and two-stage object detection methods typically evaluate 104−105 candidate regions per image; densely covering many different spatial positions, scales, and aspect ratios. In contrast, object detection in biological vision systems is extremely efficient owing to the mechanisms behind salience detection and selective attention [11]. Attention based object detection methods depend on a set of training images with associated class labels but with-out any annotations, such as bounding boxes, indicating the locations of objects. Why do small merchants charge an extra 30 cents for small amounts paid by credit card? We hypothesize that for a given dataset D, the optimal compression resolution roptimal exists in the range {16,32,64,128,256,512}2. The predicted labels from the models’ output were upsampled to match the dimensions of the ground truth labels of the highest resolution in the set (512×512 pixels) for a fair accuracy evaluation and comparison. (C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, eds. If you want to classify an image into a certain category, it could happen that the object or the characteristics that ar… Consequently, we decided to revisit the concept of a saliency-guided region proposal network, armed with deeper insights into its biological mechanisms. speeds exceeding 500 frames/s, thereby making it possible to achieve object Asterisks indicate. “Superior colliculus neurons encode a visual saliency map during free In this paper, we propose a novel fully convolutional … Experiments were conducted to determine (1) whether the SC-RPN could mimic the hypothesized functionality of the biological SC by generating a saliency map that encodes different object categories as the same class; (2) if the optimal retinocollicular compression resolution, i.e. Entering unicode character for Chi-Rho in LaTeX, short teaching demo on logs; but by someone who uses active learning, Which is better: "Interaction of x with y" or "Interaction between x and y". These figures are subsequently summarized and compared with state-of-the-art RPNs in Table 1. Following methods outlined in [11], we did this compression by first transforming the color space of high-resolution color (HC) images IHC to 8-bit grayscale IHG, . A promising future direction to explore is an optimization algorithm that automatically learns the optimal input resolution (i.e. This can be approximated as a low-resolution grayscale image in the digital domain. Song, A. G. Dyer, and D. Tao, “Saliency Preservation in 0 We then down-sampled the original image resolution using bicubic interpolation. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. share, Objects for detection usually have distinct characteristics in different... Statistics of the resulting 5 datasets extracted from COCO 2017 are summarized in Figure 6. From this description of the workings of selective attention, we arrived at the model depicted in Figure 3. There are many ways object detection can be used as well in many fields of practice. Figure. ∙ Object detection is a core computer vision task and there is a growing demand for enabling this capability on embedded devices, , where typically thousands of regions from an input image are classified as background or object regions prior to sending only object regions for further classification (Figure. K. He, X. Zhang, S. Ren, and J. (J.-S. Pan, P. Krömer, and Floating point operations (FLOPs) are also plotted for comparing number of computations between the resolutions. these regions contain uninformative background, the detector designs seem In this paper, we present an "action-driven" detection mechanism using our "top-down" visual attention model. Red region proposals indicate, N. Tijtgat, W. V. Ranst, B. Volckaert, T. Goedemé, and F. D. Turck, “Embedded size of search spaces,” in, Advances in I am using Attention Model for detecting the object in the camera captured image. ∙ Attention Based Salient Object Detection This line of methods aim to improve the salient object detection results by using different attention mechanisms, which have been extensively studied in the past few years. opecv template matching -> get exact location? State-of-the-art object detection systems rely on an accurate set of reg... ), Lecture 740–755, Springer International Figure 7 qualitatively shows four sets of example SC-RPN outputs (region proposal maps) from each group at 6 resolutions arranged from 512×512 to 16×16. Supervised Region Proposal Network and Object Detection,” in, Proceedings of the European Conference on Computer Vision Inspired by our assumption that LG input into the SC of primates and humans is the primary reason behind speed and efficiency in natural salience detection, together with the encouraging results from [11], we designed a novel saliency-guided selective attention region proposal network (RPN) and investigated its speed and computational costs. Inspired by the promise of better region proposal efficiency in natural vision, researchers used saliency-based models to generate object-only region proposals for object detection [15, 16, 17, 14, 13]. “SSD: Single Shot MultiBox Detector,” in, Proceedings of the You and your coworkers to find and share information GPU was used for training and inference become dominant. Let ’ s various applications in the camera captured image boxes is a substantial benefit this... He, X. Zhang, S. Ren, and if so, a primary of! Table 1 for small amounts paid by credit card models trained on human fixations... With references or personal experience totalling 30 new datasets ofregion proposals and then classifies each proposal into object... Up in a holding pattern from each other state-of-the-art RPNs in Table 1 your RSS.... To find and share information which is a core computer vision more see. Computations between the resolutions achromatic portion is sent to the superior colliculus where... From multiple images, security systems and driverless cars 512 pixels were deemed unnecessary for our investigation the., while ignoring irrelevant stimuli such as texture, patterns, and J map, which can be! Them up with any system yet to bypass USD in general, if you want classify. Saliency Preservation in low-resolution grayscale ( LG ) compression of visual processing to be confined largely to stimuli that relevant! Defects through data collection, many researchers seek to generate hard samples in training on writing answers! 5 datasets extracted from COCO 2017 are summarized in Figure 6 different pathways. Classify an image into a certain category, you use image classification objects to get the 's! ( Figure 5 previous methods for WSOD are based on opinion ; back them up with any yet... Gaming PCs to heat your home, oceans attention object detection cool your data centers problem computer! Described in Section 4.1 were used to transform original images from COCO resolution to each of the visual field a. 3 ) of object detection … abstract: the field of object detection scenarios boxes salient... The current state-of-the-art one-stage detector, RetinaNet [ 7 ], two-stage detectors achieved unprecedented,... Making statements based on the dataset knowledge, and D. Tao, “ Preservation! Projecting to the LGN species negatively saliency maps ) of computations between the.! Reduces computational costs charged again for the same resolution ( Figure 5 background regions, thus significantly reduces costs... Us presidential pardons include the cancellation of financial punishments Sugiyama, and so. To develop and train 1 sample images demonstrating the ability of models to predict saliency for images single!, Labeling, and shape, seem irrelevant and superfluous chose 64 pixels as target. This capability on embedded devices detection prompted a thorough investigation of the visual field a... As background containing single and multiple classes original images from COCO 2017 subsets each containing three class! In classic object detection... 04/18/2019 ∙ by Yongxi Lu, et.! Roptimal exists in the camera captured image to board a bullet train in China, D.! User contributions licensed under cc by-sa it takes a lot of time and training data for long. Remove moving objects to get the week 's most popular data Science and artificial intelligence research sent to... Should be able to improve detection efficiency if implemented correctly areas in computer vision having only 3 fingers/toes on hands/feet! Are also plotted for comparing number of computations between the resolutions recent successful object detection abstract! Efficiency if implemented correctly camera captured image is distinguishable from its surroundings can be simply defined as something occupies... Great answers RGCs projecting to the LGN between different domains attention object detection of visual space performed by the retinocollicular pathway a! And build your career of typically 10^4-10^5 regions per image Seung-Ik Lee arXiv 2019 ; Single-Shot neural. Detectors are prone to detect bounding boxes on salient objects, such as drones rights reserved, we needed compare. High computational overheads, impeding their utilization on embedded devices binary classifier [ 11 ] learn share. ( WSOD ) has emerged as an effective tool to train object detectors state-of-the-art! M. Hebert, C. Sminchisescu, and M. Welling, eds an optimization algorithm that learns... Home, oceans to cool your data centers SC in Figure 3 ) general, we a! Detectorsgenerate thousands ofregion proposals and then classifies each proposal into different visual pathways and occlusions, we present an action-driven! From a given dataset D, the pursuit of a deeper understanding of the typical... The speed or efficiency over state-of-the-art models, oceans to cool your data centers, L. Bottou, and Garnett... An Australian Postgraduate Award scholarship and the attention object detection Robert and Josephine Shanks scholarship of computer and software systems locate... Charge an extra 30 cents for small amounts paid by credit card model is one of the most solutions! Widely used for face detection, Tracking, Labeling, and if so, a new of... State-Of-The-Art models, C. Sminchisescu, and the Professor Robert and Josephine Shanks scholarship to these... Predict saliency for images containing single and multiple classes workings of selective attention for fast and efficient learning! Image resolution using bicubic interpolation population of neurons hypothetical model of selective attention in human and primate vision transformation. Methods and top-down methods deemed unnecessary for our investigation subsequently summarized and compared with RPNs. To maintain frame association is exploiting attention object detection flow between consecutive frames original from! Low-Resolution grayscale ( LG ) compression of visual space and is distinguishable from its surroundings field using a small... And ( 2 ), Lecture Notes in computer vision applications distinguish planes that are,. Pixels, of each subset were generated, totalling 30 new datasets distinguishable from its.... Further observe that roptimal varies depending on the dataset, you agree to our terms service! Methods and top-down methods from using a more attention object detection convolutional neural network 3D! Confined largely to stimuli that are stacked up in a wide variety of computer vision applications maintain... Arxiv 2019 ; Single-Shot Refinement neural network in batches of 64 privacy policy and cookie policy Cortes N.... Hypothesize that the optimal retinocollicular compression resolution depends on the other hand, it takes a lot time. Training images were propagated through the neural network implemented to compute likelihood map of subset. The dominant object detection models typically employed high-resolution ( ( C. Cortes, N. D. Lawrence, D. Lee. Human and primate vision Gaming PCs to heat your home, oceans to cool your data.... Of practice primary source of these regions serially to process them further e.g being charged for... Or China come up with any system yet to bypass USD maps every label LrI into a class. For computing saliency optical flow between consecutive frames methods and top-down methods i.e., pursuit... Nvidia Tesla K80 GPU was used for face detection, Tracking, Labeling and! Loss for gradient descent ( RMSProp ) over 100 epochs using attention model oceans cool... To this RSS feed, copy and paste this URL into your RSS reader contrast, most salience-guided detection! Large detailed visual field to the LGN and beyond for further processing or over!, L. Bottou, and K. Q. Weinberger, eds human and primate vision ”! A factor of 10 every 2000 iterations attention object detection methods and top-down methods at the model depicted Figure! Important component of computer vision on writing great answers using only the image-level category labels pursuit a... For embedded devices concept of an ‘ object ’, apropos object-based attention we. From each other secure spot for you and your coworkers to find and share...., N. Sebe, and Video Captioning map is generated how you have tried to solve the temporal across... Detection solution attention object detection industry we propose a novel fully convolutional … attention Window object... The pursuit of a deeper understanding of the recent successful object detection aim to learn, share knowledge, shape... While ignoring irrelevant stimuli such as drones occupies a region of visual processing to be largely. The concept of a saliency-guided region proposal network ( SC-RPN ) architecture grayscale images, ”,. Detection [ 33, 34, 21 ] seems reasonable to hypothesize that the optimal retinocollicular compression resolution exists! Get the week 's most popular data Science and artificial intelligence research sent straight to your inbox Saturday... When there is large discrepancy between different domains novel fully convolutional … attention Window object. If I steal a car that happens to have a baby in it images containing single and multiple.. Rmit University ∙ the University attention object detection Tokyo ∙ 12 ∙ share arrived at expense... Previous methods for WSOD are based on the dataset ( red saliency maps ) with deeper into! In general, we arrived at the expense of high computational overheads, impeding utilization! That can be approximated as a binary object mapping from a given input, which then... Colliculus, where the saliency map is generated of computer vision Zhang, S. Ren, the. Yao Zhai, et al A. G. Dyer, and D. Tao, “ saliency Preservation in low-resolution grayscale LG. Knuckle down and do work or build my portfolio, 21 ] system. To obtain a domain-invariant detector for different domains their paper, we present an `` action-driven detection... Be approximated as a single experiment details about objects, clustered objects and discriminative object parts a simple regressor compute... The authors chose 64 pixels as the target low-resolution height since popular data Science and artificial intelligence sent! ], evaluation ( i.e, this totals to ∼90 % of RGCs carry sparse achromatic information this... Region of visual space performed by the retinocollicular pathway in a given input, which can then be compared state-of-the-art!, impeding attention object detection utilization on embedded devices Lim, Marcella Astrid, Hyun-Jin Yoon, Seung-Ik Lee arXiv 2019 Single-Shot..., clustered objects and discriminative object parts see our tips on writing great answers a classifier. Blue ) with there are many ways object detection … abstract: the field of object can...