Object proposals for detecting moving or static video objects need to address issues such as speed, memory complexity and temporal consistency. We propose an efficient Video Object Proposal (VOP) generation method and show its efficacy in learning a better video object detector. A deep-learning based video object detector learned using the proposed VOP achieves state-of-the-art detection performance on the Youtube-Objects dataset. We further propose a clustering of VOPs which can efficiently be used for detecting objects in video in a streaming fashion. As opposed to applying per-frame convolutional neural network (CNN) based object detection, our proposed method called Objects in Video Enabler thRough LAbel Propagation (OVERLAP) needs to classify only a small fraction of all candidate proposals in every video frame through streaming clustering of object proposals and class-label propagation. Source code will be made available soon.
Unsupervised category-independent detection proposals are evidently shown to be effective for object detection in images. Some of these methods are Objectness @cite_26 , category-independent object proposals @cite_8 , SelectiveSearch @cite_9 , MCG @cite_25 , GOP @cite_33 , BING @cite_18 , EdgeBoxes @cite_19 . A comparative literature survey on object proposal methods and their evaluations can be found in @cite_38 @cite_15 . Although there is no best'' detection proposal method, EdgeBoxes, which scores windows based on edge content, achieve better balance between recall and repeatabilty.
@cite_38 @cite_18 @cite_26 @cite_33 @cite_8 @cite_9 @cite_19 @cite_15 @cite_25
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Object proposals have recently emerged as an essential cornerstone for object detection. The current state-of-the-art object detectors employ object proposals to detect objects within a modest set of candidate bounding box proposals instead of exhaustively searching across an image using the sliding window approach. However, achieving high recall and good localization with few proposals is still a challenging problem. The challenge becomes even more difficult in the context of autonomous driving, in which small objects, occlusion, shadows, and reflections usually occur. In this paper, we present a robust object proposals re-ranking algorithm that effectivity re-ranks candidates generated from a customized class-independent 3DOP (3D Object Proposals) method using a two-stream convolutional neural network (CNN). The goal is to ensure that those proposals that accurately cover the desired objects are amongst the few top-ranked candidates. The proposed algorithm, which we call DeepStereoOP, exploits not only RGB images as in the conventional CNN architecture, but also depth features including disparity map and distance to the ground. Experiments show that the proposed algorithm outperforms all existing object proposal algorithms on the challenging KITTI benchmark in terms of both recall and localization. Furthermore, the combination of DeepStereoOP and Fast R-CNN achieves one of the best detection results of all three KITTI object classes. HighlightsWe present a robust object proposals re-ranking algorithm for object detection in autonomous driving.Both RGB images and depth features are included in the proposed two-stream CNN architecture called DeepStereoOP.Initial object proposals are generated from a customized class-independent 3DOP method.Experiments show that the proposed algorithm outperforms all existing object proposals algorithms.The combination of DeepStereoOP and Fast R-CNN achieves one of the best detection results on KITTI benchmark. We are motivated by the need for an object proposal generation algorithm that achieves a good balance between proposal localization quality, object recall and computational efficiency. We propose a novel object proposal algorithm BING++ which inherits the good computational efficiency of BING BingObj2014 but significantly improves its proposal localization quality. Central to our success is based on the observation that good bounding boxes are those that tightly cover objects. Edge features, which can be computed efficiently, play a critical role in this context. We propose a new algorithm that recursively improves BING's proposals by exploiting the fact that edges in images are typically associated with object boundaries. BING++ improves proposals recursively by incorporating nearest edge points (to proposal boundary pixels) to obtain a tighter bounding box. This operation has linear computational complexity in number of pixels and can be done efficiently using distance transform. Superpixel merging techniques are then employed as post-processing to further improve the proposal quality. Empirically on the VOC2007 dataset, using @math proposals and IoU threshold 0.5, our method achieves 95.3 object detection recall (DR), 79.2 mean average best overlap (MABO), and 68.7 mean average precision (mAP) on object detection over 20 object classes within an average time of 0.009 seconds per image. Current high-quality object detection approaches use the scheme of salience-based object proposal methods followed by post-classification using deep convolutional features. This spurred recent research in improving object proposal methods. However, domain agnostic proposal generation has the principal drawback that the proposals come unranked or with very weak ranking, making it hard to trade-off quality for running time. This raises the more fundamental question of whether high-quality proposal generation requires careful engineering or can be derived just from data alone. We demonstrate that learning-based proposal methods can effectively match the performance of hand-engineered methods while allowing for very efficient runtime-quality trade-offs. Using the multi-scale convolutional MultiBox (MSC-MultiBox) approach, we substantially advance the state-of-the-art on the ILSVRC 2014 detection challenge data set, with @math mAP for a single model and @math mAP for an ensemble of two models. MSC-Multibox significantly improves the proposal quality over its predecessor MultiBox method: AP increases from @math to @math for the ILSVRC detection challenge. Finally, we demonstrate improved bounding-box recall compared to Multiscale Combinatorial Grouping with less proposals on the Microsoft-COCO data set. The use of object proposals is an effective recent approach for increasing the computational efficiency of object detection. We propose a novel method for generating object bounding box proposals using edges. Edges provide a sparse yet informative representation of an image. Our main observation is that the number of contours that are wholly contained in a bounding box is indicative of the likelihood of the box containing an object. We propose a simple box objectness score that measures the number of edges that exist in the box minus those that are members of contours that overlap the box’s boundary. Using efficient data structures, millions of candidate boxes can be evaluated in a fraction of a second, returning a ranked set of a few thousand top-scoring proposals. Using standard metrics, we show results that are significantly more accurate than the current state-of-the-art while being faster to compute. In particular, given just 1000 proposals we achieve over 96 object recall at overlap threshold of 0.5 and over 75 recall at the more challenging overlap of 0.7. Our approach runs in 0.25 seconds and we additionally demonstrate a near real-time variant with only minor loss in accuracy.