Detection and Localization of Anamoly in Videos Using Fruit Fly Optimization-Based Self Organized Maps

Detection of anomalies in crowded videos has become an eminent field of research in the community of computer vision. Variation in scene normalcy obtained by training labeled and unlabelled data is identified as Anomaly by diverse traditional approaches. There is no hardcore isolation among anomalous and non-anomalous events; it can mislead the learning process. This paper plans to develop an efficient model for anomaly detection in crowd videos. The video frames are generated for accomplishing that, and feature extraction is adopted. The feature extraction methods like Histogram of Oriented Gradients (HOG) and Local Gradient Pattern (LGP) are used. Further, the meta-heuristic training-based Self Organized Map (SOM) is used for detection and localization. The training of SOM is enhanced by the Fruit Fly Optimization Algorithm (FOA). Moreover, the flow of objects and their directions are determined for localizing the anomaly objects in the detected videos. Finally, comparing the state-of-the-art techniques shows that the proposed model outperforms most competing models on the standard video surveillance dataset.