# Fast and Improved Real-Time Vehicle Anti-Tracking System
## 1 Introduction  
As the number of vehicles increases, the demand for driver assistance systems also increases, ensuring safe and comfortable driving. Over the last decades, many criminals have been resorting to the method of stealing cars commonly referred to as carjacking. This most frequently happens while the owner is driving the vehicle and is temporarily stopped, e.g., at a traffic light, gas station, convenience store, ATM, hotel, etc. Carjacking can be prevented by equipping a vehicle with an anti-tracking system which alerts the victim vehicle to being tracked by suspicious criminals. From this perspective, the intelligent transportation industry has conducted various studies. This is achieved by installing high-tech equipment and other control systems on the vehicle rather than on the road. In this case, many visual sensors are used [1]. The vision system installed in the transport vehicle can provide the location and size of other vehicles in the traffic environment, as well as provide information from roads, traffic lights, and other road users. All this information captured by the vision system is very useful in the event of traffic incidents (such as an accident or carjacking) [2]. The proposed system uses information from the visual system represented by one camera. This provides a picture and video of the scene behind the car. The purpose of the system is to identify vehicles located in this space and following the host vehicle. However, some factors are obstacles to the reliability and robustness of the tracking system, such as:  
1. Occlusion. Some parts of the vehicle of interest may be blocked by objects or other vehicles [3]. In our application, placing the camera behind the car will create many occultations between the vehicles. 2 External conditions. Color, intensity, texture, and lighting vary from image to image. The following vehicle is subject to many variations; not only the extrinsic changes produced by the visual system (lighting conditions, location of the camera), but also the intrinsic differences in the vehicle type (color, height, etc.) must be taken into account [4]. 3. Position of the object of interest. In an image, a vehicle can be seen from the front, in profile, or from any angle. The system must also be able to deal with low-resolution images, which usually make it difficult to detect and recognize an object accurately [5]. 4. Computation load. The exhaustive search of potential positions of vehicles in the full picture is prohibitive for real-time applications. The response speed is measured, in fact, for the entire system: most of the execution of all processes is done in \"real-time\". Here, the term implies that the system is capable to detect and recognize a following vehicle present in the vehicle environment before it reaches it, so the driver is warned of a dangerous situation. Many researchers have tackled these issues in their work to get better results. [PERSON] et al. proposed a method based on there being motion in an image [6]. In such a case, the intensity value of a pixel will be different from the corresponding pixel in the next frame or the reference frame. Generally, the subtraction of the background is achieved as follows: An initial model is created to model the background image. The difference between the current image and the background model is estimated to spot moving objects; and finally, the background template is updated. The regions thus detected form probable objects of interest. [PERSON] et al. proposed an object tracking algorithm that takes an image from a camera and converts it to a pre-processed image in grayscale [7]. Then the image goes through the process of canny-edge detection. If the number of vehicles is large enough after filtration and expansion, that area is selected and vehicles are detected on the image in the form of a bounding box. Finally, the algorithm uses a blob analysis for the detection of a separate number of vehicles.