Robust novel multiple human tracking algorithm published in IEEE Transactions on Multimedia
2016-01-01T00:00:00Z (GMT) by
Video based multiple human tracking often involves several challenges including target number variation, object occlusions, and noise corruption in sensor measurements. In this paper, we propose a novel method to address these challenges based on probability hypothesis density (PHD) ﬁltering with a Markov chain Monte Carlo (MCMC) implementation. More speciﬁcally, a novel social force model (SFM) for describing the interaction between the targets is used to calculate the likelihood within the MCMC resampling step in the prediction step of the PHD ﬁlter, and a one class support vector machine (OCSVM) is then used in the update step to mitigate the noise in the measurements,where the SVM is trained with features from both colour and oriented gradient histograms. The proposed method is evaluated and compared with state-of-the-art techniques using sequences from the CAVIAR, TUD and PETS2009 datasets based on the mean Euclidean tracking error on each frame, the optimal subpattern assignment (OSPA) metric, and the multiple object tracking precision (MOTP) metric. The results show improved performance of the proposed method over the baseline algorithms including the traditional particle PHD ﬁltering method, the traditional SFM based particle ﬁltering method, multi-Bernoulli ﬁltering and an online-learning based tracking method.