Performance optimization for pedestrian detection on degraded video using natural scene statistics

We evaluate the effects of transmission artifacts such as JPEG compression and additive white Gaussian noise on the performance of a state-of-the-art pedestrian detection algorithm, which is based on integral channel features. Integral channel features combine the diversity of information obtained from multiple image channels with the computational efficiency of the Viola and Jones detection framework. We utilize "quality aware" spatial image statistics to blindly categorize distorted video frames by distortion type and level without the use of an explicit reference. We combine quality statistics with a multiclassifier detection framework for optimal pedestrian detection performance across varying image quality. Our detection method provides statistically significant improvements over current approaches based on single classifiers, on two large pedestrian databases containing a wide variety of artificially added distortion. The improvement in detection performance is further demonstrated on real video data captured from multiple cameras containing varying levels of sensor noise and compression. The results of our research have the potential to be used in real-time in-vehicle networks to improve pedestrian detection performance across a wide range of image and video quality.