An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

Abstract

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

Counting dense object of multiple types based on feature enhancement

“…The achievements of dense object counting research have been widely used in the fields of traffic flow prediction, public safety management, biology and medicine-pharmacy, and supermarket monitoring and management. The research is also an integrated multidisciplinary research direction, including artificial intelligence, computer vision, machine learning, deep learning, and pattern recognition ( Dirir et al, 2021 ). Therefore, dense object counting has become one of the research hotspots with significant attention and development potential in recent years.…”

Traffic Management: Multi-Scale Vehicle Detection in Varying Weather Conditions Using YOLOv4 and Spatial Pyramid Pooling Network

“…To automate this task machine and deep learning involves developing computational models which are trained by learning class-specific features using huge amount of annotated data and then localizing and generating bounding boxes around each instance and label them. This makes object detection a super task of many other smaller tasks such as image captioning [42][43][44][45], object tracking [46][47][48][49], instance segmentation [50][51][52] and instance counting [53][54][55][56].…”