We propose a multi-camera event detection framework that can operate on a common ground plane as well as on the image plane. The proposed event detector is based on an object-centric state modeling that uses a continuous distribution hidden Markov model (CDHMM). Video objects are first detected using statistical change detection and then tracked using graph matching. Next, the algorithm recognizes events by estimating the most likely object state sequence using a HMM decoding strategy, based on the Viterbi algorithm. We demonstrate and evaluate the proposed framework on standard event detection datasets with single and multiple cameras, with both overlapping and non-overlapping fields of view.
['Murtaza Taj', 'Andrea Cavallaro']
Multi-Camera Scene Analysis using an Object-Centric Continuous Distribution Hidden Markov Model