Following a person is a fundamental requirement for human-robot interaction. In this paper we propose a novel tracking approach for robust human feet tracking which integrates human locomotion into tracking algorithms. The vertical displacement between the two feet is analyzed and we observe that this displacement during the walking cycle is close to a modulated cosine waveform. Based on this, we propose an adaptive model for the human walking pattern. We divide the motion of the human feet into local motion and global motion. The local motion is modeled by a modified cosine wave that updates along time. Global motion is estimated by the continuity between successive frames. This model is combined with particle filtering to guide the searching of the feet. A 2D Gaussian mask is generated according to the predicted position estimated by the motion model and used to modify the weight of the particles. Experiments are implemented in several human walking videos and the algorithm is evaluated against the generic particle filtering method. Results show that the feet can be tracked successfully with significant improvements compared to the generic particle filtering method.

This paper presents a novel video-based face recognition algorithm by using a sequential sampling and updating scheme, named sequential sample consensus. The proposed algorithm aims at providing a sequential scheme that can be applied to streaming video data. Different from existing approaches, the training video sequences serve as the sample space, and the person's identity in the testing sequence is characterized using an identity probability mass function (PMF) that is sequentially updated. For each testing frame, samples are randomly drawn from the sample space, and the numbers of samples for each identity are determined by the identity PMF. The testing frame is evaluated against the drawn samples to calculate the weights, and the sample weights are used for updating the identity PMF. Benefiting from the sampling procedure, the change in both the numbers and the weights of the samples for each individual leads to quick reaction of the algorithm. The proposed algorithm is robust against misclassification caused by pose variations, and sensitive to identity switching during recognition. The algorithm is evaluated using both public and self-made datasets, and shows better performance than other video-based face recognition approaches.

This paper presents a novel video-based face recognition algorithm using a sequential sampling and updating scheme, named sequential sample consensus (SSC). Different from the existing approaches, the training video sequences serve as the sample space, and the person's identity in the testing sequence is characterized by an identity probability mass function (PMF) that is sequentially updated. For each testing frame, samples are randomly drawn from the sample space with the numbers of samples for each identity determined by the identity PMF. The testing frame is evaluated against the drawn samples to calculate the weights, and the sample weights are utilized for updating the identity PMF. The proposed algorithm is robust against misclassification caused by pose variations, and sensitive to identity switching during recognition. The algorithm is evaluated using both public and self-made databases, and shows better performance than other video-based face recognition approaches.