Robust Algorithms for Counting and Detection of Moving Vehicles using Deep Learning

Abstract

Automatic detection and counting of moving objects in a video is a challenging task and has become key application area of deep learning algorithms as far as overwhelming number of vehicles on road and crime detection problems are concerned. In this paper, details of an intelligent agent are discussed which is developed for detection and counting of moving vehicle in a video stream captured through surveillance camera. The agent that we have developed works in two phases; first is object detection and second is the counting of moving vehicles. For object detection, we have applied state-of-the-art Deep Learning object detection algorithm Single Shot Detector (SSD). For counting of vehicles, we have devised two different algorithms which take input from object detection part. The first counting algorithm is an add-on for robustness on naive approach while the later one is an improvement regarding removal of dependency on capture environment parameters such as imaginary lines, direction of moving vehicles etc. Input to the agent is fed through surveillance camera in the form of video stream. We have used transfer learning by adopting standard Convolutional Neural Network models trained over COCO dataset. The algorithms that we developed for counting of moving vehicles are discussed and empirical results are presented which show their efficacy. We have also discussed the challenges that were faced in counting of moving vehicles in the video stream.