# Efficient and Safe Strategies for Intersection Management: A Review
## 3 Efficiency
### Jam Detection and Congestion Avoidance
#### 3.1.1 Jam Detection  
In any transportation hub system, using a unitary technology to detect whether the road is congested has considerable errors. At present, the mainstream detection technologies used to identify congestion are mainly divided into three categories: Image processing, sensing techniques, and probe-vehicles-based techniques. The idea of image processing is to capture real-time images with a camera or video to calculate the density of cars so as to infer whether congestion will occur. Image processing chiefly consists of three methods: The first is background extraction, which provides complete information about the external features, but the problem is that this technique is particularly sensitive to changes due to illumination and external events. To solve this problem, [PERSON] et al. introduce an adaptive update of background extraction and propose an improved algorithm based on historical statistics combining the multi-frame average method [17]. In [18], the author developed a method to extract jam events by analyzing the road background feature using multiple background images. The extraction of background image is done by performing a difference on three consecutive frames. In the extracted background image the corner feature is analyzed to detect traffic jam. Results of the experiments show that the algorithm is robust and in real time. Besides, a scheme for assessing the state of congestion from the operation of cross-correlation between successive frames transmission in a CCTV (closed-circuit television) system is proposed [19]. The tests of this  
Figure 5: Typical scene of congestion ahead of the HV. method are realized on a standardized dataset at different times of the day. Thus the results obtained make it possible to alert about traffic jams. Simple background image extraction is unstable in urban traffic videos, so the improved algorithm overcomes the image blur and the difficulty of tracking at the peak of traffic when utilizing the simple multi-frame average method. The Background Local Binary Pattern (BGLBP) handles a large number of illumination changes that occur during background extraction, and the algorithm is evaluated in three different ways [20]. The results shows that background extraction becomes more precise than before. A new method [21], which fits under the framework of mathematical morphology, is based on a recently developed textural descriptor termed as the Morphological Texture Contrast (MTC). In this work authors have employed the bright and dark top-hat transformations to handle the bright and dark features separately. Both bright and dark features extracted are subjected to the MTC operator for identifying the texture components, which in turn are used to enhance the textured parts of the original input image. Subsequently, this method is employed to segment the bright and dark textured regions separately from the two enhanced versions of the input image and the partial segmentation results obtained are combined to constitute the final segmentation result. Nonetheless, the vision-based approach faces severe changes caused by illumination, shadows, swaying trees, and moving clouds. Therefore, the authors propose a real-time algorithm [22] that performs background and front-segment segmentation without using background modeling to overcome the above problems. In the first step, an improved block-based frame difference method is established to quickly detect moving targets without being affected by rapid illumination changes. In the next step, the dual foreground fusion method is used for accurate target region extraction. In the third step, a texture-based target segmentation method is used, which separates each car from the merged foreground image spots and removes the shadows. In the fourth step, a pseudo foreground filtering method based on the concept of motion entropy is proposed to remove the false target region caused by the rickety tree or the moving cloud.