text: Deep learning methods have achieved great success in pedestrian detection, owing to its ability to learn features from raw pixels. However, they mainly capture middle-level representations, such as pose of pedestrian, but confuse positive with hard negative samples, which have large ambiguity, e.g. the shape and appearance of tree trunk' or wire pole' are similar to pedestrian in certain viewpoint. This ambiguity can be distinguished by high-level representation. To this end, this work jointly optimizes pedestrian detection with semantic tasks, including pedestrian attributes (e.g. carrying backpack') and scene attributes (e.g. road', tree', and horizontal'). Rather than expensively annotating scene attributes, we transfer attributes information from existing scene segmentation datasets to the pedestrian dataset, by proposing a novel deep model to learn high-level features from multiple tasks and multiple data sources. Since distinct tasks have distinct convergence rates and data from different datasets have different distributions, a multi-task objective function is carefully designed to coordinate tasks and reduce discrepancies among datasets. The importance coefficients of tasks and network parameters in this objective function can be iteratively estimated. Extensive evaluations show that the proposed approach outperforms the state-of-the-art on the challenging Caltech and ETH datasets, where it reduces the miss rates of previous deep models by 17 and 5.5 percent, respectively.
  @cite_34: 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.
  @cite_10: Pedestrian detection is a problem of considerable practical interest. Adding to the list of successful applications of deep learning methods to vision, we report state-of-the-art and competitive results on all major pedestrian datasets with a convolutional network model. The model uses a few new twists, such as multi-stage features, connections that skip layers to integrate global shape information with local distinctive motif information, and an unsupervised method based on convolutional sparse coding to pre-train the filters at each stage.
  @cite_21: Feature extraction, deformation handling, occlusion handling, and classification are four important components in pedestrian detection. Existing methods learn or design these components either individually or sequentially. The interaction among these components is not yet well explored. This paper proposes that they should be jointly learned in order to maximize their strengths through cooperation. We formulate these four components into a joint deep learning framework and propose a new deep network architecture. By establishing automatic, mutual interaction among components, the deep model achieves a 9 reduction in the average miss rate compared with the current best-performing pedestrian detection approaches on the largest Caltech benchmark dataset.
summary: Deep learning methods can learn features from raw pixels to improve the performance of pedestrian detection. For example, ConvNet @cite_10 employed convolutional sparse coding to unsupervised pre-train CNN for pedestrian detection. Ouyang al @cite_34 jointly learned features and the visibility of different body parts to handle occlusion. The JointDeep model @cite_21 designed a deformation hidden layer for CNN to model mixture poses information. Unlike the previous deep models that formulated pedestrian detection as a single binary classification task, TA-CNN jointly optimizes pedestrian detection with related semantic tasks, and the learned features are more robust to large variations, as shown in Fig. (c) and (d).