Detecting objects becomes difficult when we need to deal with large shape deformation, occlusion and low resolution. We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples of highly deformable objects), ii) describe them in terms of body parts, and iii) detect them when their body parts are hard to detect (e.g., animals depicted at low resolution). We represent the holistic object and body parts separately and use a fully connected model to arrange templates for the holistic object and body parts. Our model automatically decouples the holistic object or body parts from the model when they are hard to detect. This enables us to represent a large number of holistic object and body part combinations to better deal with different "detectability" patterns caused by deformations, occlusion and or low resolution. We apply our method to the six animal categories in the PASCAL VOC dataset and show that our method significantly improves state-of-the-art (by 4.1 AP) and provides a richer representation for objects. During training we use annotations for body parts (e.g., head, torso, etc), making use of a new dataset of fully annotated object parts for PASCAL VOC 2010, which provides a mask for each part.
There is a considerable body of work on part-based object detectors. Some of these methods represent an object with a holistic object ( root") template attached to a fixed number of parts @cite_0 @cite_22 @cite_30 . The main disadvantage of these methods is that they are not robust against occlusion. @cite_4 propose a DPM that allows missing parts. Our method is different from theirs as we consider of parts instead of , and our model is more flexible as we can switch off the root" as well. We also show significantly better results. @cite_12 propose a grammar model to handle a variable number of parts. The difference between our method and theirs is that we consider occlusion of body parts, while they model occlusion for latent parts of the model. @cite_11 , the authors propose a multi-resolution model to better detect small objects, where the parts are switched off at small scales. In contrast, we do not explicitly incorporate size into the model and let the model choose whether the parts are useful to describe an object or not.
@cite_30 @cite_4 @cite_22 @cite_0 @cite_12 @cite_11
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Detecting objects becomes difficult when we need to deal with large shape deformation, occlusion and low resolution. We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples of highly deformable objects), ii) describe them in terms of body parts, and iii) detect them when their body parts are hard to detect (e.g., animals depicted at low resolution). We represent the holistic object and body parts separately and use a fully connected model to arrange templates for the holistic object and body parts. Our model automatically decouples the holistic object or body parts from the model when they are hard to detect. This enables us to represent a large number of holistic object and body part combinations to better deal with different "detectability" patterns caused by deformations, occlusion and or low resolution. We apply our method to the six animal categories in the PASCAL VOC dataset and show that our method significantly improves state-of-the-art (by 4.1 AP) and provides a richer representation for objects. During training we use annotations for body parts (e.g., head, torso, etc.), making use of a new dataset of fully annotated object parts for PASCAL VOC 2010, which provides a mask for each part. Part-based models have demonstrated their merit in object detection. However, there is a key issue to be solved on how to integrate the inaccurate scores of part detectors when there are occlusions or large deformations. To handle the imperfectness of part detectors, this paper presents a probabilistic pedestrian detection framework. In this framework, a deformable part-based model is used to obtain the scores of part detectors and the visibilities of parts are modeled as hidden variables. Unlike previous occlusion handling approaches that assume independence among visibility probabilities of parts or manually define rules for the visibility relationship, a discriminative deep model is used in this paper for learning the visibility relationship among overlapping parts at multiple layers. Experimental results on three public datasets (Caltech, ETH and Daimler) and a new CUHK occlusion dataset1 specially designed for the evaluation of occlusion handling approaches show the effectiveness of the proposed approach. This paper present a part-based approach for detecting objects with large variation of appearance. We extract local image patches as local features both from the object and from the background in training images to learn an object part model discriminatively. Our object part model discriminates the local features whether they are an object part or not. Based on the discrimination results, each local feature casts probabilistic votes for the object location and size which are learned from the training images. Our object part model also requires regression performance for predicting the object location and size through the voting procedure. We build such an object part model with an ensemble of randomized trees trained by splitting each tree node so as to reduce the entropy of class label distribution and the variance of object location and size. Experimental results on hand detection with large pose variation show that our approach outperforms conventional generalized Hough transform. We verified the performance on a public dataset of side-view cars. Several popular and effective object detectors separately model intra-class variations arising from deformations and appearance changes. This reduces model complexity while enabling the detection of objects across changes in view- point, object pose, etc. The Deformable Part Model (DPM) is perhaps the most successful such model to date. A common assumption is that the exponential number of templates enabled by a DPM is critical to its success. In this paper, we show the counter-intuitive result that it is possible to achieve similar accuracy using a small dictionary of deformations. Each component in our model is represented by a single HOG template and a dictionary of flow fields that determine the deformations the template may undergo. While the number of candidate deformations is dramatically fewer than that for a DPM, the deformed templates tend to be plausible and interpretable. In addition, we discover that the set of deformation bases is actually transferable across object categories and that learning shared bases across similar categories can boost accuracy.