Bio-inspired head detection framework based on online learning algorithm

• Published in: Multimedia tools and applications 2020-07, Vol.79 (27-28), p.19509-19536
• Main Authors: Luo, Dapeng, Mou, Quanzheng, Zeng, Zhipeng, Luo, Chen, Wei, Longsheng, Zhang, Xiangli
• Format: Article
• Language: English
• Subjects: Algorithms; Classifiers; Computer Communication Networks; Computer Science; Data integration; Data Structures and Information Theory; Distance learning; Machine learning; Modules; Multimedia Information Systems; Object recognition; On-line systems; Performance enhancement; Sensors; Special Purpose and Application-Based Systems; Tracking; Verification
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-08744-6

Online learning algorithms have been widely used to address vision-related issues such as object detection and tracking. However, a robust online learning object detection system that can continuously improve performance through self-learning continues to elude designers. This study proposes a novel online learning framework, which combines detection and verification modules to train a scene-specific head detector on a fly. For the detection module, a proposed online bootstrap cascade classifier is employed as the object detector of the framework. The cascade decision strategy is used to integrate a number of weak online classifiers. The resulting system contains sufficient weak classifiers and maintains a low computation cost. During the online learning process, the complexity of the cascade structure adapts to the difficulty of the detection task. For the verification module, a simple yet effective particle filter tracking algorithm, based on information fusion, is used to automatically label online learning samples produced by detection responses. With this method, the object detector improves detection performance by autonomously learning the samples. The online head detection framework is ported to the NVIDIA Jetson TK1 embedded platform, which enables the platform to recognize different head postures through self-learning. Experimental results on three video datasets demonstrate the effectiveness of the framework.