Urban areas are the most interesting problem domains due to their proclivity for having a high density of vehicles and recurrent obstruction. However, because the vehicles use predictable paths, tracking them as they move across a city is simple.
In this article, we are going to discuss the most efficient tracking techniques that take advantage of the predictability and act as a real-time multi-camera system.
It’s an idea developed from recent studies that utilizes the urban environment to efficiently track it with a camera. Since vehicles usually travel on a lane, breaking down the 2D tracking issues into a set of 1D tracking issues is possible. This involves coming up with tracks that capture the motion of vehicles within a single lane and generating complete tracks, inclusive of changes in lanes, through connecting the relevant tracks. Nevertheless, to connect tracks, the preceding task operates by using an ad hoc scoring technology that is limited to a single camera.
Major Real-Time Contributions
• Lane-based tracking
Tracking structure within a strict anticipated framework and establishing image systems to geo-referenced systems to express initial consistent units in preference to units of pixels per frame.
• Offering a novel measure for the possibility of two tracks coming from the same vehicle.
• Providing quantitative analysis of the thousands of cars with hand tracks that are travelling through several camera views.
This analysis highlights the advantage of tracking within a collective camera network since there are fewer errors within the system as compared to when they are operating on the same algorithm within each camera.
This system provides an extensive approach to the city scale and real-time vehicle monitoring within the city. These high quality products could help in optimizing traffic light patterns and typical traffic behaviour models on the ground, which is useful in civic planning as well as collecting data after detecting any unusual behaviors.
Multi-Object Tracking
This tracking system uses database methods. To develop object tracks, these methods make use of a wide range of optimization techniques. Using a combined function to determine which one to associate is the most common element to these approaches. Typically, the results are based on an integration of appearance and kinematics alongside ad hoc terms. The most relevant results are capable of connecting overlapping and non-overlapping tracks.
Multiple Camera Tracking
Multiple camera tracking can lessen ambiguities existing in the single view case significantly. It involves deploying various cameras with consideration to overlapping, partly overlapping, as well as non-overlapping points of view.
To perform data analysis in global coordinates and map objects to the ground, this system exploits a standard approach.
Bottom Line
A real-time system disintegrates a 2D tracking difficulty into a group of 1D tracking issues assuming that vehicles in urban areas often move within lanes. Ideally, this framework is appropriate for implementation in camera network as it provides a procedure to access data from a real-time live video stream using a small set of tracks.
While there are many other approaches to tracking vehicles in multi-camera networks, there is no other method that can perform both the detection and connection tasks.
