English

Abstract

The fast and robust recognition of human actions is an important aspect for many video-based applications in the field of human computer interaction and surveillance. Although current recognition algorithms provide more and more advanced results, their usability for on-line applications is still limited. To bridge this gap a online video-based action recognition system is presented that combines histograms of sparse feature point flow with an HMM-based action recognition. The usage of feature point motion is computational more efficient than the more common histograms of optical flow (HoF) by reaching a similar recognition accuracy. For recognition we use low-level action units that are modeled by Hidden-Markov-Models (HMM). They are assembled by a context free grammar to recognize complex activities. The concatenation of small action units to higher level tasks allows the robust recognition of action sequences as well as a continuous on-line evaluation of the ongoing activity. The average runtime is around 34 ms for processing one frame and around 20 ms for calculating one hypothesis for the current action. Assuming that one hypothesis per second is needed, the system can provide a mean capacity of 25 fps. The systems accuracy is compared with state of the art recognition results on a common benchmark dataset as well as with a marker-based recognition system, showing similar results for the given evaluation scenario. The presented approach can be seen as a step towards the on-line evaluation and recognition of human motion directly from video data.