Simultaneous tracking and activity recognition

TY - GEN

T1 - Simultaneous tracking and activity recognition

AU - Manfredotti, Cristina Elena

AU - Fleet, David J.

AU - Hamilton, Howard J.

AU - Zilles, Sandra

N1 - Conference code: 23

PY - 2011

Y1 - 2011

N2 - Many tracking problems involve several distinct objects interacting with each other. We develop a framework that takes into account interactions between objects allowing the recognition of complex activities. In contrast to classic approaches that consider distinct phases of tracking and activity recognition, our framework performs these two tasks simultaneously. In particular, we adopt a Bayesian standpoint where the system maintains a joint distribution of the positions, the interactions and the possible activities. This turns out to be advantegeous, as information about the ongoing activities can be used to improve the prediction step of the tracking, while, at the same time, tracking information can be used for online activity recognition. Experimental results in two different settings show that our approach 1) decreases the error rate and improves the identity maintenance of the positional tracking and 2) identifies the correct activity with higher accuracy than standard approaches.

AB - Many tracking problems involve several distinct objects interacting with each other. We develop a framework that takes into account interactions between objects allowing the recognition of complex activities. In contrast to classic approaches that consider distinct phases of tracking and activity recognition, our framework performs these two tasks simultaneously. In particular, we adopt a Bayesian standpoint where the system maintains a joint distribution of the positions, the interactions and the possible activities. This turns out to be advantegeous, as information about the ongoing activities can be used to improve the prediction step of the tracking, while, at the same time, tracking information can be used for online activity recognition. Experimental results in two different settings show that our approach 1) decreases the error rate and improves the identity maintenance of the positional tracking and 2) identifies the correct activity with higher accuracy than standard approaches.

U2 - 10.1109/ICTAI.2011.36

DO - 10.1109/ICTAI.2011.36

M3 - Article in proceedings

SN - 978-0-7695-4596-7

T3 - Proceedings - International Conference on Tools with Artificial Intelligence, TAI

SP - 189

EP - 196

BT - Proceedings - 2011 23rd IEEE International Conference on Tools with Artificial Intelligence

PB - IEEE

T2 - 23rd IEEE International Conference on Tools with Artificial Intelligence

Y2 - 7 November 2011 through 9 November 2011

ER -