Video surveillance system plays a pivotal role in automatic detection of abandoned luggage/bag in public places which causes threats to the public. As, the terrorist attacks are increasing world-wide, the detection and prevention of such attack is necessary to safeguard the people in public places. In this, a novel framework for the detection and classification of static object is proposed. In the proposed work first the static objects are identified and then it is classified to check the detected object is bag or anything else. In this study, the performance of machine learning techniques like Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbour, and Random Forest methods are analyzed. The performance is tested in standard (PETS 2006, PETS 2007 and AVSS i-LIDS) and custom datasets. The SVM and ANN produce best results in terms of classification and accuracy. Applications of various machine learning algorithms could clearly assist for identification and prevention of terrorist attacks in public places.

视频监视系统在自动检测公共场所遗弃的行李/行李中起着举足轻重的作用，这对公众构成威胁。随着世界范围内恐怖袭击的增加，为保护公共场所的人们，必须侦查和预防此类袭击。在此，提出了一种用于静态物体检测和分类的新颖框架。在提出的工作中，首先要识别静态对象，然后将其分类以检查检测到的对象是袋子还是其他东西。在这项研究中，分析了机器学习技术（如支持向量机（SVM），人工神经网络（ANN），K最近邻和随机森林法）的性能。性能已在标准（PETS 2006，PETS 2007和AVSS i-LIDS）和自定义数据集中进行了测试。SVM和ANN在分类和准确性方面产生最佳结果。各种机器学习算法的应用显然可以帮助识别和预防公共场所的恐怖袭击。