Abstract
Head detection in real-world videos is an important research topic in computer vision. However, existing studies face some challenges in complex scenes. The performance of head detectors deteriorates when objects which have similar head appearance exist for indoor videos. Moreover, heads have small scales and diverse poses, which increases the difficulty in detection. To handle these issues, we propose Motion-Guided Pseudo Siamese Network for Indoor Video Head Detection (MGPSN), an end-to-end model to learn the robust head motion features. MGPSN integrates spatial-temporal information on pixel level, guiding the model to extract effective head features. Experiments show that MGPSN is able to suppress static objects and enhance motion instances. Compared with previous methods, it achieves state-of-the-art performance on the crowd Brainwash dataset. Different backbone networks and detectors are evaluated to verify the flexibility and generality of MGPSN.
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URL
https://arxiv.org/abs/2110.03302
