Indirect activity recognition using a target-mounted camera

We present a new method to recognize activity patterns from video acquired by a camera mounted on the target (i.e., activity performer). Because of this unconventional camera setting, algorithms for activity recognition must be redesigned because the activity performer never appears in the video. We approach this recognition problem indirectly by observing background changes in the acquired image sequences. A motion histogram scheme is proposed to characterize activity patterns from the perspective of camera motion. This histogram is utilized as the input to our activity identification algorithm based on a hidden Markov model. Our experimental results show that our method successfully identifies complex activities even the motion profile of an activity involves a large variance. Our method is applied to the construction of a new wearable device that helps people lose weight and maintain a healthy lifestyle by automatically recognizing and monitoring physical activity.