Abstract
Trajectory recognition of the moving objects is the basic problem of the activity analysis. To recognize the incomplete trajectory caused by the video frame loss or the occlusion, we use the asynchronous hidden Markov model (AHMM) to improve the recognition accuracy. Multi-target trajectory observations are obtained using background subtraction method in which the background model is generated in HSV color space for better shadow control. To ensure the validity of the comparison between the AHMM and the hidden Markov model (HMM), the same initial parameter set is adopted in EM algorithms for each method. The hidden states of the AHMM are estimated by the E-step. Finally, the maximum likelihood of the test samples relative to all the trained models is computed, the maximum value is saved, and the corresponding model is the recognition result. Experiments indicate that the AHMM performs better than the HMM in the recognition of the incomplete trajectory.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China under Grant No. 61104213 and the National Natural Science Foundation of Jiangsu Province under Grant No. BK2011146.
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Qin, P., Chen, Y. (2014). Trajectory Recognition Based on Asynchronous Hidden Markov Model. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_47
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DOI: https://doi.org/10.1007/978-3-642-54924-3_47
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