Modified Ant Colony Optimization for Human Recognition in Videos of Low Resolution Katakam Ranganarayana 1* , Gurrala Venkateswara Rao 2 1 Department of IT, GITAM Deemed to be University, Visakhapatnam 530045, India 2 Department of CSE, GITAM Deemed to be University, Visakhapatnam 530045, India Corresponding Author Email: katakam916@gmail.com https://doi.org/10.18280/ria.360510 ABSTRACT Received: 16 September 2022 Accepted: 16 October 2022 Privacy protections for people filmed in public settings is a prerequisite to widespread camera use. For this reason, low-resolution videos are used from which specific people can be reliably obscured. Since the human region in low-resolution videos comprises of so few pixels and so little information, human detection is more challenging there than it is in high- resolution videos. With the current state of affairs, one of the most important challenges is tracking a target from lower resolution movies. Identification or monitoring of persons in low-resolution movies has become a common issue in many domains due to a lack of appropriate data. This study presents a novel people-detection algorithm that makes use of low-resolution film to overcome the aforementioned problem. In the first stage, a three-step procedure is executed, the video data gathered from low-resolution videos from various form of data is considered. The captured video is separated into frames and transformed from RGB to gray-scale. Local Binary Pattern (LBP) method is used in the second phase to accomplish background subtraction. Thirdly the feature extraction is performed in which histogram of optical flow (HOF)and some of the features are extracted in the form of eigen values. Finally these features are optimized using Modified Ant Colony Optimization (MACO) model to remove the unwanted features and select global features. Finally, classification operation is performed using Support Vector Machine (SVM) classifier to recognize the person from lower resolution videos. The results obtained using the implemented MACO-SVM obtains good results when compared with existing techniques with rate of accuracy 91.46% for soccer dataset, 90.8% for KTH dataset and 89.75% accuracy using VIRAT dataset. Keywords: video surveillance, local binary pattern, ant colony optimization, support vector machine 1. INTRODUCTION Many efforts have been done in the field of identifying the objects in motion and compliance over the last few decades to make the following dependable, robust, and successful: surveillance of videos, robotics, multifactor authentication, multimedia creation, biomedical sciences, and so on [1]. However, there are other problems that present roadblocks to enhancing these applications. As analyzed by Jiang et al. [2] these issues might include lighting changes, dynamic backgrounds, concealment, space closure, shadow, and so on. These obstacles grow more severe while tracking of objects in videos with lower resolution. It's tough to identify exactly what you're looking for in a low-resolution video since a majority of distinguishing data, such as visual and unique content is lost. It results to the incorrect investigation, which leads to the discovery of a defective occurrence. Some of the major benefits by using low resolution videos for performing research is been discussed by Park et al. [3] and they are less memory used for storage of videos, time for transmission of data will be reduced and lesser time interval. The majority of typical tracking algorithms rely on higher resolution video (HRV) to extract a straight line as designed by Chen et al. [4] and Cremers et al. [5] the bag of words. However, because they function in higher-resolution frames, these approaches necessitate additional computation expenses. Other approaches in the literature employ lesser quality movies as input, but these videos are eventually enhanced to greater resolution with the use of higher resolution techniques, demonstrating that they are less expensive. Many approaches in atypical human detection literature, such as employ categories of classification for recognizing events rather than lower-resolution input video [6, 7]. The training database needs to closely watch and the researchers need study time. Other solutions, analyzed by Lili Cui et al. [8], need human configuration at the start of the event's default program and have substantial computing expenditures. According to the preceding literature, a novel comprehensive algorithm for detecting objects in low quality videos is being developed, which will aid in the development of a completely automated surveillance system. The author introduced two effective methods for recognition of movement in lower resolution films recorded from data under surveillance implemented by Ranganarayana et al. [9], with the goal of providing security. However, a set of limitations are induced for every algorithm. The Standard FCM method, for example, does not incorporate any sort of information which is spatial in the context of image, causing the data to be vulnerable to noise and other imaging abnormalities. Second, the mean shift method is only reliant on colour features, limiting its effectiveness to background subtraction. Further SVM classification is used by Schuldt et al. [10] for identification of human in low resolution videos. The evolution of optimization techniques helps in improving Revue d'Intelligence Artificielle Vol. 36, No. 5, October, 2022, pp. 731-736 Journal homepage: http://iieta.org/journals/ria 731