Time-scale invariant modeling and classifying for object behaviors in 3D space based on monocular vision

We present an approach to classify 3D behaviors online under monocular vision. We estimate similarity transformation between frames by matched markers, then transforms the similarity matrixes to logarithmic space to generate unified parameter sequence with 4 degrees of freedom. To eliminate the sensitivity of duration time, we formulate a time-scale invariant feature (TSIF) based on polygonal approximation algorithm, and implement online feature picking-up with dynamic programming. In the recognition phase, we use dynamic time warping to train the behavior templates with limited categories then recognize the test sequences. The experimental results show that the class separability of the proposed behavior template is increased by at least 60% to the comparative approaches, furthermore, recognizing unknown behaviors in continuous video online is achieved.