Unexpected Human Behavior Recognition in Image Sequences Using Multiple Features

This paper presents a novel approach for unexpected behavior recognition in image sequences with attention to high density crowd scenes. Due to occlusions, object-tracking in such scenes is challenging and in cases of low resolution or poor image quality it is not robust enough to efficiently detect abnormal behavior. The wide variety of possible actions performed by humans and the problem of occlusions makes action recognition unsuitable for behavior recognition in high density crowd scenes. The novel approach, which is presented in this paper uses features based on motion information instead of detecting actions or events in order to detect abnormality. Experiments demonstrate the potentials of the approach.