计算机科学
人工智能
深度学习
卷积神经网络
学习迁移
机器学习
计算机视觉
残差神经网络
跟踪(教育)
特征提取
目标检测
模式识别(心理学)
作者
Shubhangi Kale,Raghunathan Shriram
出处
期刊:Advances in intelligent systems and computing
日期:2020-12-15
标识
DOI:10.1007/978-3-030-73689-7_21
摘要
Tracking objects in video surveillance is a challenging task for public security. The advent of the semantic approach has greatly raised the growth of anomaly detection. However, the current anomaly detection methods typically experience problems like inadequate use of movement patterns and inconsistency on various datasets. This research proposed a system to enhance the efficiency of anomaly detection in video surveillance. The proposed system consists of two parts that involves object tracking and suspicious activity detection. The overall framework detects and tracks the abnormal objects in video surveillance. The transfer learning-based ResNet tracking has been used for object tracking. Distance Metric Learning (DML) method has been used for detecting suspicious activities in video surveillance. The results are estimated to analyze the efficiency of the proposed method. The proposed network classifier is compared with the existing ResNet and VGG-16 network. The proposed method provides 99% accuracy that had high performance compared to other existing methods.
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