## 1 Introduction
In other words, the premise of realizing communication in an image-sensor-based VLC system with different background interferences is to track the target signal source LED and extract the target area accurately and completely from received images, while it is usually ignored in the exiting studies to our knowledge. Moreover, given the various attractive advantages of complementary metal-oxide-semiconductor (CMOS) sensors [21], a CMOS sensor is used as the receiver in this paper. A complete and practical image-sensor-based OVLC system contains many components, including modulation of the signal source information, tracking and extraction of signal sources, features extraction and demodulation of signal source image area, image processing and denoising, etc. Each of these parts has great research value and has much room for improvement. As mentioned above, most existing reports have conducted different degrees of research on various aspects of image-sensor-based OVLC system while focus less on specialized tracking and extraction method ofsignal sources, which is vitally important and is the premise of communication especially in dynamic scenarios. Moreover, the core of a tracking and extraction method is the tracking algorithm, which is a fairly broad field and has a great many aspects to study and improve. Thus, an accurate, fast, stable, and practical target tracking algorithm is needed in the area of OVLC. Motivated by these often-overlooked but important and pressing problems, in this paper, we propose an improved tracking algorithm based on the Cam-Shift algorithm and Kalman filter to track and extract the target signal source LED from received images with interference in an outdoor dynamic scenario, aiming at promoting the feasibility of VLC applications in outdoor scenes, and filling in the gaps in this research area, the Cam-Shift algorithm converts received original images to color probability distribution maps based on the color histogram of the signal source LED by considering the target's color characteristic and special distribution characteristic. Then, it adaptively adjusts the location and size of the search window based on the results obtained from the previous frame. At the same time, the motion state of the target in the next frame is predicted with a Kalman filter according to the position and velocity information of the current frame to enhance the accuracy and robustness of tracking. Furthermore, it locates the final position of the target signal source LED and extracts the target area. Different from our previous work [4] where a Kalman filter was used as an auxiliary correction tool to combine measurement position information via optical flow and predicted position information via Bayesian forecast by using a weighted least square method to obtain the final positioning information, in this paper, outcome of Kalman filter is used directly as measurement to update the filter when severe similar color interference or occlusion occur; otherwise, the result of Cam-Shift algorithm is used directly as a measurement to update the Kalman filter. The remainder of this paper is organized as follows. Section 2 introduces detailed theories of OVLC and the proposed algorithm. Experimental setup and results analysis are presented in Section 3. Finally, this paper is summarized in Section 4.