Covariance descriptors on moving regions for human detection in very complex outdoor scenes

The detection of humans in very complex scenes can be very challenging, due to the performance degradation of classical motion detection and tracking approaches. An alternative approach is the detection of human-like patterns over the whole image. The present paper follows this line by extending Tuzel et al.'s technique based on covariance descriptors and LogitBoost algorithm applied over Riemannian manifolds. Our proposal represents a significant extension of it by: (a) exploiting motion information to focus the attention over areas where motion is present or was present in the recent past; (b) enriching the human classifier by additional, dedicated cascades trained on positive and negative samples taken from the specific scene; (c) using a rough estimation of the scene perspective, to reduce false detections and improve system performance. This approach is suitable in multi-camera scenarios, since the monolithic block for human-detection remains the same for the whole system, whereas the parameter tuning and set-up of the three proposed extensions (the only camera-dependent parts of the system), are automatically computed for each camera. The approach has been tested on a construction working site where complexity and dynamics are very high, making human detection a real challenge. The experimental results demonstrate the improvements achieved by the proposed approach.