Computer vision based traffic monitoring system for multi-track freeways

Nowadays, development is synonymous with construction of infrastructure. Such road infrastructure needs constant attention in terms of traffic monitoring as even a single disaster on a major artery will disrupt the way of life. Humans cannot be expected to monitor these massive infrastructures over 24/7 and computer vision is increasingly being used to develop automated strategies to notify the human observers of any impending slowdowns and traffic bottlenecks. However, due to extreme costs associated with the current state of the art computer vision based networked monitoring systems, innovative computer vision based systems can be developed which are standalone and efficient in analyzing the traffic flow and tracking vehicles for speed detection. In this article, a traffic monitoring system is suggested that counts vehicles and tracks their speeds in realtime for multi-track freeways in Australia. Proposed algorithm uses Gaussian mixture model for detection of foreground and is capable of tracking the vehicle trajectory and extracts the useful traffic information for vehicle counting. This stationary surveillance system uses a fixed position overhead camera to monitor traffic

Midrange exploration exploitation searching particle swarm optimization with HSV-template matching for crowded environment object tracking

Particle Swarm Optimization (PSO) has demonstrated its effectiveness in solving the optimization problems. Nevertheless, the PSO algorithm still has the limitation in finding the optimum solution. This is due to the lack of exploration and exploitation of the particle throughout the search space. This problem may also cause the premature convergence, the inability to escape the local optima, and has a lack of self-adaptation in their performance. Therefore, a new variant of PSO called Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO) was proposed to overcome these drawbacks. In this algorithm, the worst particle will be relocating to a new position to ensure the concept of exploration and exploitation remains in the search space. This is the way to avoid the particles from being trapped in local optima and exploit in a suboptimal solution. The concept of exploration will continue when the particle is relocated to a new position. In addition, to evaluate the performance of MEESPSO, we conducted the experiment on 12 benchmark functions. Meanwhile, for the dynamic environment, the method of MEESPSO with Hue, Saturation, Value (HSV)-template matching was proposed to improve the accuracy and precision of object tracking. Based on 12 benchmarks functions, the result shows a slightly better performance in term of convergence, consistency and error rate compared to another algorithm. The experiment for object tracking was conducted in the PETS09 and MOT20 datasets in a crowded environment with occlusion, similar appearance, and deformation challenges. The result demonstrated that the tracking performance of the proposed method was increased by more than 4.67% and 15% in accuracy and precision compared to other reported works