Video Surveillance (VS) systems play a crucial role in maintaining security in public spaces, commercial establishments and residential areas. Detecting and preventing human-related crimes within the footage captured by these systems is a challenging task. Traditionally, VS systems rely on basic motion detection, which often leads to false alarms and inefficient use of resources. Loitering, a behavior frequently associated with criminal activities, requires more nuanced detection to reduce false positives and improve response times. Accurate tracking of individuals, especially in crowded environments, is another challenge. The chief objective of this research is to address these challenges by introducing an innovative Loitering- based Human Crime Detection (LHCD) module in VS. This module combines enhanced euclidean based Deep Simple Online Real-time Tracking (DSORT) with the Segmentation Quality Assessment (SQA) algorithm to accurately assess human travel distances. Also, this research integrates the Beluga Whale Adam Dingo Optimizer (BWADO) and a Deep Convolutional Neural Network (DCNN) to boost the precision and efficiency of Human Crime Detection (HCD) within loitering areas. The introduced approach demonstrates the effectiveness of introduced module, which reduces false alarms and enhances response times in VS. Outcomes demonstrate that the introduced approach outperforms existing approaches in various performance measures like accuracy (99.76%), F1-score (99.89%), recall (98.59%), precision (98.9%) and processing time (1.78s) demonstrating its superior effectiveness and potential for advancements in the field.

[1] Abdulghafoor N. and Abdullah H., “A Novel Real- Time Multiple Objects Detection and Tracking Framework for Different Challenges,” Alexandria Engineering Journal, vol. 61, no. 12, pp. 9637- 9647, 2022. https://doi.org/10.1016/j.aej.2022.02.068

[2] Abdullah F. and Jalal A., “Semantic Segmentation Based Crowd Tracking and Anomaly Detection Via Neuro-Fuzzy Classifier in Smart Surveillance System,” Arabian Journal for Science and Engineering, vol. 48, no. 2, pp. 2173-2190, 2023. https://doi.org/10.1007/s13369-022-07092-x

[3] Ahmed I., Ahmad M., Ahmad A., and Jeon G., “Top View Multiple People Tracking by Detection Using Deep Sort and YOLOv3 with Transfer Learning: Within 5G Infrastructure,” International Journal of Machine Learning and Cybernetics, vol. 12, no. 11, pp. 3053-3067, 2021. https://doi.org/10.1007/s13042-020-01220-5

[4] Ahmed S., Bhatti M., Khan M., Lövström B., and Shahid M., “Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos,” Applied Sciences, vol. 12, no. 12, pp. 1-21, 2022. https://doi.org/10.3390/app12125772

[5] Ahmed W. and Yousaf M., “A Deep Autoencoder- Based Approach for Suspicious Action Recognition in Surveillance Videos,” Arabian Journal for Science and Engineering, vol. 49, no. 3, pp. 3517-3532, 2024. https://doi.org/10.1007/s13369-023-08038-7

[6] Amin J., Anjum M., Ibrar K., Sharif M., Kadry S., and Crespo R., “Detection of Anomaly in Surveillance Videos Using Quantum Convolutional Neural Networks,” Image and Vision Computing, vol. 135, pp. 104710, 2023. https://doi.org/10.1016/j.imavis.2023.104710

[7] Biswas T., Bhattacharya D., and Mandal G., “Dynamic Strategy to Use Optimum Memory Space in Real-Time Video Surveillance,” Journal of Ambient Intelligence and Humanized Loitering Based Human Crime Detection in Video Surveillance using Beluga Whale ... 367 Computing, vol. 14, no. 3, pp. 2771-2784, 2023. https://doi.org/10.1007/s12652-023-04521-z

[8] Cai C., Gou B., Khishe M., Mohammadi M., Rashidi S., Moradpour R., and Mirjalili S., “Improved Deep Convolutional Neural Networks Using Chimp Optimization Algorithm for COVID19 Diagnosis from the X-Ray Images,” Expert Systems with Applications, vol. 213, pp. 119206, 2023. https://doi.org/10.1016/j.eswa.2022.119206

[9] Fathy C. and Saleh S., “Integrating Deep Learning- Based IOT and Fog Computing with Software- Defined Networking for Detecting Weapons in Video Surveillance Systems,” Sensors, vol. 22, no. 14, pp. 1-22, 2022. https://doi.org/10.3390/s22145075

[10] Gandapur M., “E2E-VSDL: End-to-End Video Surveillance-Based Deep Learning Model to Detect and Prevent Criminal Activities,” Image and Vision Computing, vol. 123, pp. 104467, 2022. https://doi.org/10.1016/j.imavis.2022.104467

[11] Georgiou A., Masters P., Johnson S., and Feetham L., “Uav‐Assisted Real‐time Evidence Detection in Outdoor Crime Scene Investigations,” Journal of Forensic Sciences, vol. 67, no. 3, pp. 1221-1232, 2022. https://doi.org/10.1111/1556-4029.15009

[12] Gómez J., Aycard O., and Baber J., “Efficient Detection and Tracking of Human Using 3D LiDAR Sensor,” Sensors, vol. 23, no. 10, pp. 1-12, 2023. https://doi.org/10.3390/s23104720

[13] Huszár V., Adhikarla V., Négyesi I., and Krasznay C., “Toward Fast and Accurate Violence Detection for Automated Video Surveillance Applications,” IEEE Access, vol. 11, pp. 18772-18793, 2023. DOI:10.1109/ACCESS.2023.3245521

[14] Ingle P. and Kim Y., “Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities,” Sensors, vol. 22, no. 10, pp. 1-21, 2022. https://doi.org/10.3390/s22103862

[15] Jaouedi N., Boujnah N., and Bouhlel M., “A Novel Recurrent Neural Networks Architecture for Behavior Analysis,” The International Arab Journal of Information Technology, vol. 18, no. 2, pp. 133-139, 2021. https://doi.org/10.34028/iajit/18/2/1

[16] Kamoona A., Gostar A., Bab-Hadiashar A., and Hoseinnezhad R., “Multiple Instance-Based Video Anomaly Detection Using Deep Temporal Encoding-Decoding,” Expert Systems with Applications, vol. 214, pp. 119079, 2023. https://doi.org/10.1016/j.eswa.2022.119079

[17] Khairuddin A., Ali N., Alwee R., Haron H., and Zain A., “Parameter Optimization of Gradient Tree Boosting Using Dragonfly Algorithm in Crime Forecasting and Analysis,” Journal of Computer Science, vol. 15, no. 8, pp. 1085-1096, 2019. https://doi.org/10.3844/jcssp.2019.1085.1096

[18] Kotkar V. and Sucharita V., “Fast Anomaly Detection in Video Surveillance System Using Robust Spatiotemporal and Deep Learning Methods,” Multimedia Tools and Applications, vol. 82, no. 22, pp. 34259-86, 2023. https://doi.org/10.1007/s11042-023-14840-0

[19] Kumar K. and Reddy H., “Crime Activities Prediction System in Video Surveillance by an Optimized Deep Learning Framework,” Concurrency and Computation: Practice and Experience, vol. 34, no. 11, 2022. https://doi.org/10.1002/cpe.6852

[20] Li Z., Zhang X., Xu F., Jing X., and Zhang T., “A Multi-Scale Video Surveillance Based Information Aggregation Model for Crime Prediction,” Alexandria Engineering Journal, vol. 73, pp. 695- 707, 2023. https://doi.org/10.1016/j.aej.2023.04.045

[21] Meng F., Guo L., Wu Q., and Li H., “A New Deep Segmentation Quality Assessment Network for Refining Bounding Box Based Segmentation,” IEEE Access, vol. 7, pp. 59514-59523, 2019. https://doi.org/10.1109/access.2019.2915121

[22] Mithoo P. and Kumar M., “Social Network Analysis for Crime Rate Detection Using Spizella Swarm Optimization Based BiLSTM Classifier,” Knowledge-Based Systems, vol. 269, pp. 110450, 2023. https://doi.org/10.1016/j.knosys.2023.110450

[23] Mumtaz A., Sargano A., and Habib Z., “Robust Learning for Real-World Anomalies in Surveillance Videos,” Multimedia Tools and Applications, vol. 82, no. 13, pp. 20303-20322, 2023. https://doi.org/10.1007/s11042-023-14425-x

[24] Nazir A., Mitra R., Sulieman H., and Kamalov F., “Suspicious Behavior Detection with Temporal Feature Extraction and Time-Series Classification for Shoplifting Crime Prevention,” Sensors, vol. 23, no. 13, pp. 1-19, 2023. https://doi.org/10.3390/s23135811

[25] Patrikar D. and Parate M., “Anomaly Detection Using Edge Computing in Video Surveillance System: Review,” International Journal of Multimedia Information Retrieval, vol. 11, no. 2, pp. 85-110, 2022. https://doi.org/10.1007/s13735- 022-00227-8

[26] Pazho A., Neff C., Noghre G., Ardabili B., Yao S., Baharani M., and Tabkhi H., “Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things,” IEEE Internet of Things Journal, vol. 10, no. 17, pp. 14940-14951, 2023. https://doi.org/10.1109/jiot.2023.3263725

[27] Pouyan S., Charmi M., Azarpeyvand A., and Hassanpoor H., “Propounding First Artificial Intelligence Approach for Predicting Robbery Behavior Potential in an Indoor Security Camera,” IEEE Access, vol. 11, pp. 60471-60489, 2023. https://doi.org/10.1109/access.2023.3284472

[28] Qasim M. and Verdu E., “Video Anomaly Detection System Using Deep Convolutional and 368 The International Arab Journal of Information Technology, Vol. 21, No. 3, May 2024 Recurrent Models,” Results in Engineering, vol. 18, pp. 101026, 2023. https://doi.org/10.1016/j.rineng.2023.101026

[29] Sahay K., Balachander B., Jagadeesh B., Kumar G., Kumar R., and Parvathy L., “A Real Time Crime Scene Intelligent Video Surveillance Systems in Violence Detection Framework Using Deep Learning Techniques,” Computers and Electrical Engineering, vol. 103, pp. 108319, 2022. https://doi.org/10.1016/j.compeleceng.2022.108319

[30] Shoitan R., Moussa M., and Nemr H., “Attribute Based Spatio-Temporal Person Retrieval in Video Surveillance,” Alexandria Engineering Journal vol. 63, pp. 441-454, 2023. https://doi.org/10.1016/j.aej.2022.07.053

[31] Singla N., Nagpal S., and Singh J., “Frame Duplication Detection Using CNN-Based Features with PCA and Agglomerative Clustering,” in Proceedings of the International Conference on Communication and Intelligent Systems, New Delhi, pp. 383-91, 2022. https://doi.org/10.1007/978-981-19-2130-8_31

[32] UCF Crime Dataset, https://www.kaggle.com/datasets/odins0n/ucf- crime-dataset, n.d, Last Visited, 2024.

[33] Ullah W., Hussain T., Min Ullah F., Khan M., Hassaballah M., Rodrigues J., Baik S., and Albuquerque V., “Ad-Graph: Weakly Supervised Anomaly Detection Graph Neural Network,” International Journal of Intelligent Systems, vol. 2023, pp. 1-12, 2023. https://doi.org/10.1155/2023/7868415

[34] Waddenkery N. and Soma S., “Adam-Dingo Optimized Deep Maxout Network-Based Video Surveillance System for Stealing Crime Detection,” Measurement: Sensors, vol. 29, pp. 1- 13, 2023. https://doi.org/10.1016/j.measen.2023.100885

[35] William P., Shrivastava A., Karpagam N., Mohanaprakash T., Tongkachok K., and Kumar K., “Crime Analysis Using Computer Vision Approach with Machine Learning,” in Proceedings of the 3rd Mobile Radio Communications and 5G Networks, Kurukshetra, pp. 297-315, 2023. https://doi.org/10.1007/978- 981-19-7982-8_25

[36] Yang Z., Liao W., Wang H., Bak C., and Chen Z., “Improved Euclidean Distance Based Pilot Protection for Lines with Renewable Energy Sources,” IEEE Transactions on Industrial Informatics, vol. 18, no. 12, pp. 8551-8562, 2022. https://doi.org/10.1109/tii.2022.3148318

[37] Zahid A., Qasim T., Bhatti N., and Zia M., “A Data-Driven Approach for Road Accident Detection in Surveillance Videos,” Multimedia Tools and Applications, vol. 83, pp. 17217-17231 2023. https://doi.org/10.1007/s11042-023-16193-0

[38] Zi X., Chaturvedi K., Braytee A., Li J., and Prasad M., “Detecting Human Falls in Poor Lighting: Object Detection and Tracking Approach for Indoor Safety,” Electronics, vol. 12, no. 5, pp. 1- 12, 2023. https://doi.org/10.3390/electronics12051259

[39] Zhong C., Li G., and Meng Z., “Beluga Whale Optimization: A Novel Nature-Inspired Metaheuristic Algorithm,” Knowledge-Based Systems, vol. 251, pp. 109215, 2022. https://doi.org/10.1016/j.knosys.2022.109215