Abstract. This paper presents an approach for human detection and simultaneous behavior recognition from images and image sequences. An action representation is derived by applying a clustering algorithm to sequences of Histogram of Oriented Gradient (HOG) descriptors of human motion images. For novel image sequences, we first detect the human by matching extracted descriptors with the prototypical action primitives. Given a sequence of assigned action primitives, we can build a histogram from observed motion. Thus, behavior can be classified by means of histogram comparison, interpreting behavior recognition as a problem of statistical sequence analysis. Results on publicly available benchmarkdata show a high accuracy for action recognition.
In REF , the authors presented the human detection and simultaneous behavior recognition from RGB image sequences by using the action representation method depended on applying the clustering algorithm to the sequence of HOG descriptor of human motion images.
Behavior histograms for action recognition and human detection
Workshop on Human Motion
Mathematics Computer Science