Automated person categorization for video surveillance using soft biometrics

We present a prototype video tracking and person categorization system that uses face and person soft biometric features to tag people while tracking them in multiple camera views. Our approach takes advantage of temporal aspect of video by extracting and accumulating feasible soft biometric features for each person in every frame to build a dynamic soft biometric feature list for each tracked person in surveillance videos. We developed algorithms for extracting face soft biometric features to achieve gender and ethnicity classification and session soft biometric features to aid in camera hand-off in surveillance videos with low resolution and uncontrolled illumination. To train and test our face soft biometry algorithms, we collected over 1500 face images from both genders and three ethnicity groups with various sizes, poses and illumination. These soft biometric feature extractors and classifiers are implemented on our existing video content extraction platform to enhance video surveillance tasks. Our algorithms achieved promising results for gender and ethnicity classification, and tracked person re-identification for camera hand-off on low to good quality surveillance and broadcast videos. By utilizing the proposed system, a high level description of extracted person's soft biometric data can be stored to use later for different purposes, such as to provide categorical information of people, to create database partitions to accelerate searches in responding to user queries, and to track people between cameras.