Abstract
Based on the improved adaptive filtering method, this paper conducts in-depth discussion and research on embedded graphics and video coding and chooses to improve the adaptive filtering algorithm from three aspects: starting point prediction, search template, and window partitioning. The algorithm is imported into the encoder for video capture and encoding. By capturing videos of different formats, resolutions, and times, the memory size of the video files collected before and after the algorithm optimization is compared, and the optimized algorithm occupies the memory space of the video file in the actual system. The conclusion of less and higher coding rates. The collected video information is stored on a personal computer equipped with a freeness, and external electronic devices only need to download and install the browser, and the collected video information can be accessed in the local area network through the protocol. The improved coding algorithm has higher coding efficiency and can reduce the storage space occupied by the video.
Highlights
Image information occupies the main part of the information that people obtain from the outside world through vision
After the 16bit image data is processed by the threshold adaptive edge detection algorithm, the data is buffered through synchronous dynamic random-access memory (SDRAM), and the real-time edge detection image is obtained through the display
The introduction of APEC index judgment based on the original KCF significantly improves the tracking accuracy of the algorithm; after the introduction of APEC+SVM based on KCF tracking, the tracking accuracy has been improved to a certain extent compared with only using APEC indexes
Summary
Image information occupies the main part of the information that people obtain from the outside world through vision. The importance of intuitive information provided by images is self-evident. The image description is a more intuitive and specific form of information expression [1]. With the rapid development of computer science and the improvement of people’s security awareness, video image acquisition equipment can be seen everywhere, and the video surveillance system composed of video acquisition equipment and computers has attracted increased attention due to its intuitive and convenient features. The current video surveillance system can monitor in realtime and detect, track, and identify moving objects [2]. The prerequisite for realizing these complex algorithms is the edge detection of the collected images. The accurate extraction of edge information provides a certain basis for the implementation of subsequent image segmentation and recognition algorithms [3].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
