The Video Based Traffic Analysis

Published Date: 02 Nov 2017

Introduction

Vehicle detection is very important for civilian and military applications, such as highway monitoring, and the urban traffic planning. For the traffic management, vehicles detection is the critical step. Vehicles detection must be implemented at different environment where the light and the traffic status changing. Vehicles detection could be achieved using the common magnetic loop detectors which are still used even though they are not the effective. Loop detectors are considered as point detectors and could not give the traffic information for the highway. Vision based techniques are more suitable than the magnetic loop sensors.

They do not disturb traffic while installed and they are easy to modify. Their applicability is more comprehensive because they could be used in many aspects as vehicle detection, counting, classification, tracking, and monitoring. One camera could be used to monitor large section of highway. In spite the apparent advantages of vision based methods there are still many challenges. These challenges are weather changes, sun light direction and intensity changes, building shadows, vehicles have different sizes, shapes and colors. In this paper one digital camera installed over the freeway to detect successive images.

Detected images could be analyzed to extract the background automatically. Each image contains background of the highway and the moving vehicles. It is difficult to get freeway image without moving vehicles (background), so the freeway background must be extracted from the sequence images. The extracted background is used in subsequent analysis to detect moving vehicles. Current approaches for vehicles detection try to overcome the environment changes. Some approaches achieve the detection using background subtraction only and predicting the background through the next update interval.

In these approaches the background is not extracted but detected and then updated through the next images processing. Intensity changes, stopped vehicles (or very slow moving vehicles) and camera moving lead to miss detection in these techniques. It is used to detect vehicle in simple scenes. Another approach uses edge-based techniques. In this approach 3D model is proposed for the vehicle. This 3D model depends on the edge detection of the vehicle. It is applicable under perfect conditions for passenger vehicles only. The edge in image processing is abrupt change in the intensity values.

The edge detection suffers from many difficulties such as vehicle shadows, dark colors and ambient lights. Edge detection process becomes more difficult when vehicle color is close to the freeway color. In other approaches such as probabilistic and statistical methods, there is not strict distribution for vehicle model so they use the better approximations for the unknown distributions, this leads to intensive computations and time consuming. The results of applied these methods will lead to high miss detection, and could not apply for complicated scenes. In these techniques, detection rate is low as compared to the other approaches. Other approaches use explicit detail model, where they need detail model and a hierarchy for detail levels.

In the model contains substructures like windshield, roof, hood, and radiometric features as color constancy between hood color and roof color (where the gray level is higher than the median of the histogram). It is apparent that a large number of models are needed to cover all types of vehicles. In a hierarchical model is used to decide on the detection step (that identifies and clusters the image pixels) which pixels have a strong probability to belong to vehicles. In this case a huge computation is needed to detect the vehicles, and this will result in miss detection for different shapes of vehicles.

In vehicle detection is implemented by calculating various characteristics features in the image of a monochrome camera. The detection process uses shadow and symmetry features of vehicle to generate vehicle hypothesis. This is beneficial for driver assistance but it is not applicable for vehicles counting and complicated scenes. In neural networks were used for vehicle detections. Neural networks have drawbacks; the main one is that there is not warranty that they reach the global minimum (in this case there are not closed-form solutions for modeling the vehicle detection).

The other one implies to learn a data set representative of the real world and there is not universal optimum model for neural network. In fuzzy measures are used to detect vehicles. The detection process depends on the light intensity value. When light intensity value falls in certain interval, fuzzy measures must be used to decide if it is a vehicle or not. When the intensity value is larger than this interval, it represents a vehicle and when it is less than this interval vehicle does not exist.

This approach suffers from environment light changes and interval determination that needed to apply the fuzzy measures. In this paper, background extraction and edge detection is used to detect vehicles. This is useful in two ways; the first is using the advantages of the background subtraction and edge detection to detect vehicles. Second one it is able to deal with complex scenes and treat the intensity hanging problems.

2. Related Work

Video-based traffic flow monitoring is a fast emerging field based on the continuous development of computer vision. A survey of the state-of-the-art video processing techniques in traffic flow monitoring is presented in this paper. Firstly, vehicle detection is the first step of video processing and detection methods are classified into background modeling based methods and non-background modeling based methods. In particular, nighttime detection is more challenging due to bad illumination and sensitivity to light. Then tracking techniques, including 3D model-based, region-based, active contour-based and feature-based tracking, are presented. A variety of algorithms including MeanShift algorithm, Kalman Filter and Particle Filter are applied in tracking process. In addition, shadow detection and vehicles occlusion bring much trouble into vehicle detection, tracking and so on. Based on the aforementioned video processing techniques, discussion on behavior understanding including traffic incident detection is carried out. Finally, key challenges in traffic flow monitoring are discussed.

We propose a new video analysis method for counting vehicles, where we use an adaptive bounding box size to detect and track vehicles according to their estimated distance from the camera given the scene-camera geometry. We employ adaptive background subtraction and Kalman filtering for road/vehicle detection and tracking, respectively. Effectiveness of the proposed method for vehicle counting is demonstrated on several video recordings taken at different time periods in a day at one location in the city of Istanbul.

On-board video analysis has attracted a lot of interest over the two last decades, mainly for safety improvement (through e.g. obstacles detection or drivers assistance). In this context, our study aims at providing a video-based real-time understanding of the urban road traffic. Considering a video camera fixed on the front of a public bus, we propose a cost-effective approach to estimate the speed of the vehicles on the adjacent lanes when the bus operates on its reserved lane. We propose to work on 1-D segments drawn in the image space, aligned with the road lanes. The relative speed of the vehicles is computed by detecting and tracking features along each of these segments, while the absolute speed of vehicles is estimated from the relative one thanks to odometer and/or GPS data. Using pre-defined speed thresholds, the traffic can be classified in real-time into different categories such as "fluid", "congestion"... As demonstrated in the evaluation stage, the proposed solution offers both good performances and low computing complexity, and is also compatible with cheap video cameras, which allows its adoption by city traffic management authorities.

Video processing has become an efficient technique support for collecting parameters of urban traffic. Detection and tracking of multiple targets with an uncalibrated CCD camera is developed in this paper. In order to obtain moving targets from the video sequence efficiently, the paper presents mixture Gaussian background model based on object-level, and moving objects are extracted after background subtraction. Moving multi-targets are tracked through integration of the motion and shape features by Kalman filter modeling. In order to ensure the continuity and the stabilization, occlusion processing is performed. The proposed approach is validated under real traffic scenes. Experimental results show that detection and tracking are robust and adaptive, can be well applied in real-world