Detecting closely spaced and occluded pedestrians using specialized deep models for counting

Pedestrian detection is an important task in surveillance applications and becomes particularly challenging when pedestrians are close together or occluding one another. This paper presents a novel approach to detect pedestrians in such challenging scenarios. A deep convolutional neural network trained for counting is specialized to count one pedestrian. The feature extractor learned thereby is exploited to detect one pedestrian at a time iteratively. For the base counting model and the specialization, extensive annotation efforts are not required since only a single number at the image level is used. Use of our method on pedestrian datasets with occlusion showed an improvement in the average miss rate values as compared to other methods for handling occlusion.