ABSTRAT:
Surveillance videos are able to capture a variety of realistic anomalies. In this paper, we propose to learn anomalies by exploiting both normal and anomalous videos. To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labelled training videos, i.e. the training labels (anomalous or normal) are at video- level instead of clip-level. In our approach, we consider normal and anomalous videos as bags and video segments as instances in multiple instance learning (MIL), and automatically learn a deep anomaly ranking model that predicts high anomaly scores for anomalous video segments. Furthermore, we introduce sparsity and temporal smoothness constraints in the ranking loss function to better localize anomaly during training. We also introduce a new large-scale first of its kind dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery, etc. as well as normal activities.
EXISTING SYSTEM :
the success of sparse representation and dictionary learning approaches in several computer vision problems, researchers in [28, 42] used sparse representation to learn the dictionary of normal behaviours. During testing, the patterns which have large reconstruction errors are considered as anomalous behaviours. Due to successful demonstration of deep learning for image classification, several approaches have been proposed for video action classification [24, 37]. However, obtaining annotations for training is difficult and laborious, specifically for videos. Recently, [18, 40] used deep learning based autoencoders to learn the model of normal behaviours and employed reconstruction loss to detect anomalies. Our approach not only considers normal behaviours but also anomalous behaviours for anomaly detection, using only weakly labelled training data
EXISTING SYSTEM DISADVANTAGES:
1.LESS ACCURACY
2. LOW EFFICIENCY
PROPOSED SYSTEM :
We propose a MIL solution to anomaly detection by leveraging only weakly labelled training videos. We pro- pose a MIL ranking loss with sparsity and smoothness constraints for a deep learning network to learn anomaly scores for video segments. • We introduce a large-scale video anomaly detection dataset consisting of 1900 real-world surveillance videos of 13 different anomalous events and normal activities captured by surveillance cameras. It is by far the largest dataset with more than 25 times videos than existing largest anomaly dataset and has a total of 128 hours of videos. • Experimental results on our new dataset show that our proposed method achieves superior performance as com- pared to the state-of-the-art anomaly detection approaches. • Our dataset also serves a challenging benchmark for activity recognition on untrimmed videos, due to the complexity of activities and large intra-class variations. We pro- vide results of baseline methods, C3D [37] and TCNN [21], on recognizing 13 different anomalous activities
PROPOSED SYSTEM ADVANTAGES:
1.HIGH ACCURACY
2.HIGH EFFICIENCY
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
