People detection and tracking is one of the important research fields that have gained a lot of attention in the last few years. Although person detection and counting systems are commercially available today, there is a need for further research to address the challenges of scenarios. Automated video surveillance system or Anomaly detection addresses observation of people within a busy environment leading to the description of their actions and interactions. It requires detection and tracking of people to ensure security, safety and site management. Object detection is one of the fundamental steps in Anomaly detection or automated video surveillance. Object detection from the video sequence is mainly performed by background subtraction technique. It is widely used approach for detecting moving objects from static cameras. As the name suggests, background subtraction is the process of separating out the foreground objects from the background in a sequence of video frames. The main aim of the Anomaly detection here is, to detect and track an object in motion by using single video recorded image. Video is recorded and fixed at the database; the background subtraction algorithm is used for segmenting moving object in video. If human entity is detected the tracking lines are formed around human and the object is tracked. The main aim is to develop anomaly detection for real-time security system.

The largest source of information in healthcare during the present epidemic is radiological imaging, which is also one of the most difficult sources to interpret. Clinicians today are forced to rely heavily on therapeutic image analysis that has been filtered and sometimes performed by worn-out radiologists. Transmission of these medical data increases in frequency due to patient overflow, and protecting confidentiality, along with integrity and availability, emerges as one of the most crucial components of security. Medical images generally contain sensitive information about patients and are therefore vulnerable to various security threats during transmission over public networks. These images must be protected before being transmitted over this network to the public. In this paper, an efficient SCAN and chaotic-map-based image encryption model is proposed. This paper describes pixel value and pixel position manipulation based on SCAN and chaotic theory. The SCAN method involves translating an image’s pixel value to a different pixel value and rearranging pixels in a predetermined order. A chaotic map is used to shift the positions of the pixels within the block. Decryption follows the reverse process of encryption. The effectiveness of the suggested strategy is evaluated by computing the histogram chi-square test, MSE, PSNR, NPCR, UACI, SSIM, and UQI. The efficiency of the suggested strategy is demonstrated by comparison analysis. The results of analysis and testing show that the proposed program can achieve the concept of partial encryption. In addition, simulation experiments demonstrate that our approach has both a faster encryption speed and higher security when compared to existing techniques.