Multiple face tracking method in the wild using color histogram features - Kwon, Hyuk Jin; Lee, Seok Hee; Hosseini, Sepidehsadat; Moon, Jaewon; Koo, Hyung Il; Cho, Nam Ik (2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017, 2018)
This paper presents a new framework for the tracking of multiple faces. In order to address this tracking problem in the challenging environments, we first adopt a robust face detector based on Multi-task Cascaded Convolutional Networks (MTCNN) and a very efficient tracker exploiting Kernelized Correlation Filters (KCF). Then, we incorporate the detector and tracker into our framework by proposing a new data association method. In our association scheme, we consider color histogram features as well as geometric overlaps, so that it works robustly in the presence of occlusions and crossovers. We conducted experiments on the selected examples of 300VW database and a challenging test video sequence (TMBS). Experimental results have shown that the proposed method works robustly for challenging scenarios in real time (49 fps).
Keywords: Association schemes; Color histogram features; Convolutional networks; Correlation filters; Data association; Multiple face tracking; Selected examples; Tracking problem