A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras

Munkhjargal Gochoo,Ahmad Jalal,Yazeed Yasin Ghadi,Kibum Kim,Syeda Amna Rizwan

doi:10.3390/app11125503

Abstract

Automatic head tracking and counting using depth imagery has various practical applications in security, logistics, queue management, space utilization and visitor counting. However, no currently available system can clearly distinguish between a human head and other objects in order to track and count people accurately. For this reason, we propose a novel system that can track people by monitoring their heads and shoulders in complex environments and also count the number of people entering and exiting the scene. Our system is split into six phases; at first, preprocessing is done by converting videos of a scene into frames and removing the background from the video frames. Second, heads are detected using Hough Circular Gradient Transform, and shoulders are detected by HOG based symmetry methods. Third, three robust features, namely, fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are extracted. Fourth, the Apriori-Association is implemented to select the best features. Fifth, deep learning is used for accurate people tracking. Finally, heads are counted using Cross-line judgment. The system was tested on three benchmark datasets: the PCDS dataset, the MICC people counting dataset and the GOTPD dataset and counting accuracy of 98.40%, 98%, and 99% respectively was achieved. Our system obtained remarkable results.

Highlights

Head and shoulders detection has become a research hotspot which plays a significant role in people counting [1] and crowd analysis which can be used for several practical applications such as surveillance, logistics and resource management coding and public transportation systems [2,3]
The results acquired on the People Counting Dataset (PCDS) dataset and Geintra Overhead ToF People Detection dataset (GOTPD) dataset exhibit greater accuracy i.e., 98.40% and 99% respectively for head tracking and counting compared to the MICC dataset which produced an acuracy rate of 98%
We have proposed an efficient method for head tracking and counting which works well despite variations in occlusion, illuminaton and background complexity

Summary

Introduction

Head and shoulders detection has become a research hotspot which plays a significant role in people counting [1] and crowd analysis which can be used for several practical applications such as surveillance, logistics and resource management coding and public transportation systems [2,3]. Many studies have been carried out on RGB image based head and shoulders counting but, due to the development of depth cameras and sensors, researchers are studying RGB-Depth images for crowd counting using head and shoulders tracking. Compared with RGB images, RGB-D images provide additional and more general depth map information for the detection of heads and shoulders. Computer vision techniques provide remarkable performance improvements to the problem of automatic head and shoulders detection and tracking in complex indoor/outdoor environments [4,5]. Research and development is usually carried out on whole body detection and counting using RGB videos which are challenged by multiple issues such as variations in occlusion, illumination, clutter, shadows etc. Head and shoulder counting using depth datasets is still a challenging task for many researchers due to various unsolved problems related to occlusions and noise

Results

Discussion

Conclusion