A real-time and accurate object detection framework, C(4), is proposed in this paper. C(4) achieves 20 fps speed and the state-of-the-art detection accuracy, using only one processing thread without resorting to special hardware such as GPU. The real-time accurate object detection is made possible by two contributions. First, we conjecture (with supporting experiments) that contour is what we should capture and signs of comparisons among neighboring pixels are the key information to capture contour cues. Second, we show that the CENTRIST visual descriptor is suitable for contour based object detection, because it encodes the sign information and can implicitly represent the global contour. When CENTRIST and linear classifier are used, we propose a computational method that does not need to explicitly generate feature vectors. It involves no image preprocessing or feature vector normalization, and only requires O(1) steps to test an image patch. C(4) is also friendly to further hardware acceleration. It has been applied to detect objects such as pedestrians, faces, and cars on benchmark data sets. It has comparable detection accuracy with state-of-the-art methods, and has a clear advantage in detection speed.

This paper presents a novel study and analysis on two important problems in gait recognition: one is how to perform gait recognition with only segment of a complete gait cycle for test and the other is how static and dynamic information affect the recognition results in such situation. In conventional gait recognition research, the gait sequence of at least one cycle is usually needed to ensure the completeness of gait dynamics. And in this paper we will show that this is not a necessary condition and considerable recognition results could still be achieved with only part of the cycle based on appropriate similarity measure algorithms. Moreover, we will also show the different roles that static and dynamic information play in such situation through experimental study. To our knowledge, there is little work on these important problems, results from which can make gait recognition more applicable in frequently happened scenarios such as occlusion.

This paper examines the multiview gait recognition problem in which human gait sequences are collected from several different views simultaneously. Motivated by the fact that set-based feature representation can handle certain intra-subject variations, we propose a new Multiview Subspace Representation (MSR) method for gait recognition across varying views and walking conditions. It takes samples collected from different views of the same subject as a feature set and uses a subspace to represent such information. Then, the similarity of two subjects is measured by the distance between two subspaces and a simple yet effective Weighted Subspace Distance (WSD) algorithm is applied to calculate the similarity. There are two notable advantages of our proposed method: 1) we need not know the exact view of the test gait sequence in advance, and 2) some extent of intra-subject variations can be effectively handled. Experimental results on two benchmark multi-view gait databases are presented to demonstrate the effectiveness of the proposed method.

We propose in this paper a novel joint subspace learning (JSL) method for view-invariant gait recognition. Inspired by the finding that if a 3-D object can be well represented by the weighted sum of a sufficiently small number of prototypes in the same view, then the representation coefficients are generally consistent across different views, we propose to use these coefficients as view-invariant features for gait recognition. Firstly, we conduct JSL to obtain the prototypes of different views. Then, we represent each sample in both the gallery set and probe set acquired from different views as a linear combination of these prototypes in the corresponding views, and extract the coefficients for feature representation. Lastly, we perform recognition by using a simple nearest neighbor rule. Experimental results on the widely used CASIA-B gait database demonstrate the effectiveness of the proposed method.

In this paper, we attempt to enhance the overall recognition rate for view-invariant gait recognition. We propose a simple but efficient framework for this task with training gait sequences from multiple views. A most important problem in the framework is about the optimal choice for the training views, that is, how many views are enough to ensure a satisfying overall performance and how to combine these views to achieve the optimal performance. To solve this problem, we execute intensive experiments and give reasonable optimal choices based on the experimental results. Besides, the gait feature descriptor and the fusion method we develop for the framework also contribute to the promising results. We propose to use mean of Radon transforms of the silhouettes as the descriptor which is very competent for view-invariant application. Moreover, the combination of class correlation and view correlation is applied to score level fusion of results from different views. The CASIA database B which contains gait data from 11 views distributed uniformly in range of [0°, 180°] is chosen in our experiments.

Gait Energy Image (GEI) has been shown to be a robust gait descriptor for gait recognition, and many algorithms based on GEI have been proposed. We propose in this paper an improved algorithm to exploit the discriminative information of GEI in identifying walking people based on gait sequences. Specifically, we first obtain the discriminative power of each pixel in the GEI, referred to as feature weight or feature score, through statistic learning from the whole gallery set. We then generate a binary mask for each frame in a gait sequence according to the intensity value of the GEI to separate the dynamic part from static part of GEI. Combining the feature score and the binary mask, we arrive at a new feature for every GEI for discriminative representation and effective recognition. Experimental results on both NLPR and USF databases show the effectiveness of our proposed algorithm in terms of gait recognition rate.