In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested on occluded images. The motivation is that there are infinite number of occlusion patterns in real world, which cannot be fully covered in the training data. So the models should be inherently robust and adaptive to occlusions instead of fitting learning the occlusion patterns in the training data. Our approach detects semantic parts by accumulating the confidence of local visual cues. Specifically, the method uses a simple voting method, based on log-likelihood ratio tests and spatial constraints, to combine the evidence of local cues. These cues are called visual concepts, which are derived by clustering the internal states of deep networks. We evaluate our voting scheme on the VehicleSemanticPart dataset with dense part annotations. We randomly place two, three or four irrelevant objects onto the target object to generate testing images with various occlusions. Experiments show that our algorithm outperforms several competitors in semantic part detection when occlusions are present.
Object detection is a fundamental task in computer vision. As the fast development of deep neural networks @cite_22 @cite_8 @cite_15 , this field has been recently dominated by one type of pipeline @cite_23 @cite_16 , which first generates a set of object proposals @cite_24 @cite_5 , and then predicts the object class of each proposal. This framework has significantly outperformed conventional approaches, which are based on handcrafted features @cite_29 and deformable part models @cite_32 @cite_1 .
@cite_22 @cite_8 @cite_29 @cite_1 @cite_32 @cite_24 @cite_23 @cite_5 @cite_15 @cite_16
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Object detection is one of the most active areas in computer vision, which has made significant improvement in recent years. Current state-of-the-art object detection methods mostly adhere to the framework of regions with convolutional neural network (R-CNN) and only use local appearance features inside object bounding boxes. Since these approaches ignore the contextual information around the object proposals, the outcome of these detectors may generate a semantically incoherent interpretation of the input image. In this paper, we propose an ensemble object detection system which incorporates the local appearance, the contextual information in term of relationships among objects and the global scene based contextual feature generated by a convolutional neural network. The system is formulated as a fully connected conditional random field (CRF) defined on object proposals and the contextual constraints among object proposals are modeled as edges naturally. Furthermore, a fast mean field approximation method is utilized to inference in this CRF model efficiently. The experimental results demonstrate that our approach achieves a higher mean average precision (mAP) on PASCAL VOC 2007 datasets compared to the baseline algorithm Faster R-CNN. Object detection is a challenging task in visual understanding domain, and even more so if the supervision is to be weak. Recently, few efforts to handle the task without expensive human annotations is established by promising deep neural network. A new architecture of cascaded networks is proposed to learn a convolutional neural network (CNN) under such conditions. We introduce two such architectures, with either two cascade stages or three which are trained in an end-to-end pipeline. The first stage of both architectures extracts best candidate of class specific region proposals by training a fully convolutional network. In the case of the three stage architecture, the middle stage provides object segmentation, using the output of the activation maps of first stage. The final stage of both architectures is a part of a convolutional neural network that performs multiple instance learning on proposals extracted in the previous stage(s). Our experiments on the PASCAL VOC 2007, 2010, 2012 and large scale object datasets, ILSVRC 2013, 2014 datasets show improvements in the areas of weakly-supervised object detection, classification and localization. Object detection is a challenging task in visual understanding domain, and even more so if the supervision is to be weak. Recently, few efforts to handle the task without expensive human annotations is established by promising deep neural network. A new architecture of cascaded networks is proposed to learn a convolutional neural network (CNN) under such conditions. We introduce two such architectures, with either two cascade stages or three which are trained in an end-to-end pipeline. The first stage of both architectures extracts best candidate of class specific region proposals by training a fully convolutional network. In the case of the three stage architecture, the middle stage provides object segmentation, using the output of the activation maps of first stage. The final stage of both architectures is a part of a convolutional neural network that performs multiple instance learning on proposals extracted in the previous stage(s). Our experiments on the PASCAL VOC 2007, 2010, 2012 and large scale object datasets, ILSVRC 2013, 2014 datasets show improvements in the areas of weakly-supervised object detection, classification and localization. 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.