Detecting pedestrians on a Movement Feature Space

•A Movement Feature Space generates oriented histogram family descriptors.•Detection system involves motion detection, hypothesis generation and validation.•A cascade of classifiers combine generative and discriminants functions.•Best result gives 25.5% of miss rate with 0.1 false positives per image.•Running time is between 2 and 6 fps in 640×480 frames size.

This work aims at detecting pedestrians in surveillance video sequences. A pre-processing step detects motion regions on the image using a scene background model based on level lines, which generates a Movement Feature Space, and a family of oriented histogram descriptors. A cascade of boosted classifiers generates pedestrian hypotheses using this feature space. Then, a linear Support Vector Machine validates the hypotheses that are likeliest to contain a person. The combination of the three detection phases reduces false positives, preserving the majority of pedestrians. The system tests conducted in our dataset, which contain low-resolution pedestrians, achieved a maximum performance of 25.5% miss rate with a rate of 10−110−1 false positives per image. This value is comparable to the best detection values for this kind of images. In addition, the processing time is between 2 and 6 fps on 640×480 pixel captures. This is therefore a fast and reliable pedestrian detector.