Traffic flow estimation and vehicle‐type classification using vision‐based spatial–temporal profile analysis

Abstract

Vision‐based traffic surveillance plays an important role in traffic management. However, outdoor illuminations, the cast shadows and vehicle variations often create problems for video analysis and processing. Thus, the authors propose a real‐time cost‐effective traffic monitoring system that can reliably perform traffic flow estimation and vehicle classification at the same time. First, the foreground is extracted using a pixel‐wise weighting list that models the dynamic background. Shadows are discriminated utilising colour and edge invariants. Second, the foreground on a specified check‐line is then collected over time to form a spatial–temporal profile image. Third, the traffic flow is estimated by counting the number of connected components in the profile image. Finally, the vehicle type is classified according to the size of the foreground mask region. In addition, several traffic measures, including traffic velocity, flow, occupancy and density, are estimated based on the analysis of the segmentation. The availability and reliability of these traffic measures provides critical information for public transportation monitoring and intelligent traffic control. Since the proposed method only process a small area close to the check‐line to collect the spatial–temporal profile for analysis, the complete system is much more efficient than existing visual traffic flow estimation methods.

Semantic Management of Urban Traffic Congestion

“…Another strategy is to use traffic signal controllers [44] at intersections to optimize the traffic flow, and introduce adaptive road routing [45] [46] [47]. Traffic congestion predictions are often performed with surveillance data [48] [49] [50] or data generated by vehicles [51], enhanced with Bluetooth technology [52].…”

Real-time hardware–software embedded vision system for ITS smart camera implemented in Zynq SoC

“…The TSI image contains information about vehicles width (x-axis) and vehicle size/speed (t-axis). This can be used to implement vehicle counting, speed estimation or even classification [41,73]. In the literature, two approaches are presented.…”

“…In the literature, two approaches are presented. The TSI image is generated from the object mask obtained using background subtraction [73] or directly using the raw image from the camera [41]. In the second case, edge detection (e.g.…”

“…6.3. The designed method was inspired by the approaches described in the literature, particularly in the works [41,73], however, it has also new elements specific for a hardware-software system.…”

Dynamic and robust method for detection and locating vehicles in the video images sequences with use of image processing algorithm

“…The precision of this method will decease during vehicle occlusion and target detection in the night and rainy weather. Yang et al [7] use background dynamic update to detect vehicle and spatial-temporal profile to calculate amount of traffic and type of vehicle. This method has a precision drop in the vehicle occlusion and dark shadows of vehicles but it is proper for mobile camera that background changes very fast.…”