On the distance metric learning between cross-domain gaits

Gait Recognition Based on Normal Distance Maps

Gait is a commonly used biometric for human recognition. Its main advantage relies on its ability to identify people at distances at which other biometrics fail. In this paper, we develop a new approach for gait recognition that combines the distance transform with curvatures of local contours. We call our gait feature template the normal distance map. Our method encodes both body shapes and boundary curvatures into a novel feature descriptor that is more robust than existing gait representations. We evaluate our approach on the widely used and challenging USF and CASIA-B datasets. Furthermore, we evaluate it on the OU-ISIR gait dataset, the largest one available in the literature, to obtain statistically reliable results. We verify our approach is significantly superior to the current state-of-the-art under most conditions.

Target Tracking via Particle Filter and Convolutional Network

We propose a more effective tracking algorithm which can work robustly in a complex scene such as illumination, appearance change, and partial occlusion. The algorithm is based on an improved particle filter which used the efficient design of observation model. Predefined convolutional filters are used to extract the high-order features. The global representation is generated by combining local features without changing their structures and space arrangements. It not only increases the feature invariance, but also maintains the specificity. The extracted feature from convolution network is introduced into particle filter algorithm. The observation model is constructed by fusing the color feature of the target and a set of features from templates which are extracted by convolutional networks without training in our paper. It is fused with the features extracted from convolutional network for tracking. In the process of tracking, the template is updated in real time, and then the robustness of the algorithm is improved. Experiments show that the algorithm can achieve an ideal tracking effect when the targets are in a complex environment.