Traffic Parameters Detection Based On Video Virtual-loop Sensors

The collection of real-time traffic data, such as traffic load, average travel speed, vehicle classification, and lane occupancy, plays a critical role in the advanced traffic management system and traveler information systems. Technological innovations have given rise to different types of traffic detectors. Conventional detectors, for example, inductive loop, detectors using ultrasonic, microwave, or infrared, have been put into use for several decades. Meanwhile, a promising approach, video-based measurement system, has developed quickly. Since it has many advantages, for instance, wider-area detection and superior flexibility, many researches have been done in this area. Previous methods are mainly based on image processing algorithms, especially on vehicle extraction and vehicle tracking. This paper is focused on developing a flexible and reliable system to detect the traffic parameters through image sequences. One of the system's ultimate goals is on intelligent image sensors and automated fast data processing. The key idea of the system is converting the two-dimensional image data to one-dimensional digital temporal signals by virtual-loops. Virtual-loop's function is similar with the inductive loop sensor. Each lane can have one or two virtual-loops to detect its traffic parameters. This method avoids the complicated vehicle identification, extraction and tracking.Virtual-loop can be freely set in the video image with its position and size adjustable. Only the image data within the virtual-loops are processed, so the time cost of calculation is reduced. Each virtual-loop's output signals mainly derive from the pixel difference between consecutive image frames within the virtual-loop area. When the result of consecutive frame difference is smaller than the threshold, current frame subtracts the background to produce the virtual-loop's signals. Through studying the traffic sample, the system itself can find the appropriate parameter to adjust the virtual-loop's signals, which restrains the noise's effect. Thus the virtual-loop's signals can reflect the passing vehicles accurately.Each virtual-loop's output signals are recorded in its special data buffer, which is updated when processing every frame. Through observing the digital string of eachdata buffer, system can detect the vehicle's appearance and track it. When two virtual-loops in the same lane all detect one vehicle's motion, system retrieve the necessary data from these two virtual-loop's data buffer to calculate the vehicle's velocity by signal correlation. In order to tracking the vehicle more inerrably, system uses two important parameters (maximum error tolerance and minimum reliability) and an effective algorithm (punishment and reward algorithm). In the end, the parameters of each lane's traffic load, vehicle velocity and vehicle classification are acquired through the surveillance of the virtual-loops output signals.The real-time prototype system is implemented on a computer. Experimental results illustrate the detection accuracy.