Real-time detection and tracking of pedestrians in CCTV images using a deep convolutional neural network

In this work, deep convolutional neural networks are used to automate the process of feature extraction from CCTV images. The extracted features serve as a strong basis for a variety of object recognition tasks and are used to address a tracking problem. The approach is to match the extracted features of individual detections in subsequent frames, hence creating a correspondence of detections across multiple frames. The developed framework is able to address challenges like cluttered scenes, change in illumination, shadows and reflection, change in appearances and partial occlusions. However, total occlusion and similar persons in the same frame remain a challenge to be addressed. The framework is able to generate the detection and the tracking results at the rate of four frames per second.