Video-Based Vehicle Detection and Tracking Using Spatiotemporal Maps

Abstract

Surveillance video cameras have been increasingly deployed along roadways over the past decade. Automatic traffic data collection through surveillance video cameras is highly desirable; however, sight-degrading factors and camera vibrations make it an extremely challenging task. In this paper, a computer-vision–based algorithm for vehicle detection and tracking is presented, implemented, and tested. This new algorithm consists of four steps: user initialization, spatiotemporal map generation, strand analysis, and vehicle tracking. It relies on a single, environment-insensitive cue that can be easily obtained and analyzed without camera calibration. The proposed algorithm was implemented in Microsoft Visual C++ using OpenCV and Boost C++ graph libraries. Six test video data sets, representing a variety of lighting, flow level, and camera vibration conditions, were used to evaluate the performance of the new algorithm. Experimental results showed that environmental factors do not significantly impact the detection accuracy of the algorithm. Vehicle count errors ranged from 8% to 19% in the tests, with an overall average detection accuracy of 86.6%. Considering that the test scenarios were chosen to be challenging, such test results are encouraging.

Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network

“…Shape-based methods have been well developed for object classification in transportation [9]. A lot of efforts have been conducted to use the features extracted from videos for object classification [10]. Gupte et al developed a rule-based method to classify vehicles into two categories: Trucks and other vehicles [9].…”

Longitudinal-Scanline-Based Arterial Traffic Video Analytics with Coordinate Transformation Assisted by 3D Infrastructure Data

“…One is the latitudinal scanline, which is defined across the traveling path ( 6 – 9 ). The other is the longitudinal scanline that is defined along the traveling direction ( 10 – 12 ). Most of the previous scanline-based vehicle detection can only produce spot-specific traffic parameters, such as volume, vehicle type, and spot speed.…”

Full-Body Occlusion Handling and Density Analysis in Traffic Video-Surveillance Systems

“…This strategy works well in dealing with shadow effects that cause overcounting because the detected window fits to the vehicles' appearance. In particular, various distinguishable features of vehicle objects are targeted for an appearance-based approach, including shape (13), points (14), edge (15), and symmetry (16). In approaches in which machine learning is implemented to classify the object, detection capability relies on visual features that are selected for training.…”