For the analysis of the interaction patterns of traffic participants, a robust visual detector and tracker for pedestrians and vehicles has been developed. The resulting implementation is currently being used to analyze hundreds of hours of recorded videos. This work concentrates on the detector for pedestrians, which combines several key concepts into a processing framework, which can run close to real-time even without GPU acceleration: a fast and efficient HOG detector cascade is combined with a deep convolutional network to combine the advantages of both algorithms. In addition to the detector, this work covers also aspects of camera calibration, which is used to control the scale of detection windows based on the viewing geometry. The evaluation of our detector on the CALTECH database as well as on real world ground truth videos and manually annotated sample data demonstrates the effectiveness of our approach.
Yuriy Lipetski, Oliver Sidla, "A combined HOG and deep convolution network cascade for pedestrian detection" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Surveillance: Applications and Algorithms, 2017, pp 11 - 17, https://doi.org/10.2352/ISSN.2470-1173.2017.4.SRV-350