A Neurobiologically Motivated Stochastic Method for Analysis of Human Activities in Video
In this paper, we develop a neurobiologically-motivated statistical method for video analysis that simultaneously searches the combined motion and form space in a concerted and efficient manner using well-known Markov chain Monte Carlo (MCMC) techniques. Specifically, we leverage upon an MCMC variant called the Hamiltonian Monte Carlo (HMC), which we extend to utilize data-based proposals rather than the blind proposals in a traditional HMC, thus creating the Data-Driven HMC (DDHMC). We demonstrate the efficacy of our system on real-life video sequences.