Multi-Person Re-Identification Based on Face, Pose and Texture Analysis in Unconstrained Videos
Jaime Gallego, Mel Slater · 2020
We present a method for re-identification of images of multiple people that appear in 2D RGB video sequences. The method needs no initialization or supervision and works with unconstrained sequences that include camera shot transitions and strong visual variations. In order to preserve tracking along the frames, our method combines facial recognition, clothing texture analysis and pose detection to compute a distance between tracked people at frame t-1, and detected people at frame t. We use techniques based on Convolutional Neural Networks (CNNs) to extract this information from the people that appear in the images. The results obtained show that the proposed method achieves accurate tracking of the people even in those difficult sequences where faces appear occluded or people present similar textures.