Automatic Unusual Activities Recognition Using Deep Learning in Academia

: In the current era, automatic surveillance has become an active research problem due to its vast real-world applications, particularly for main-taining law and order. A continuous manual monitoring of human activities is a tedious task. The use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few years. Various computer vision techniques have been applied for observation and surveillance of real-world activities. This research study focuses on detecting and recognizing unusual activities in an academic situationsuch as examinationhalls, which may help the invigilators observe and restrict the students from cheating or using unfair means. To the best of our knowledge, this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual activities. The proposed model has been named Automatic Unusual Activity Recognition (AUAR), which employs motion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional Neural Network algorithm with diverse configurations. The evaluation using standard performance measures confirm that the AUAR model outperforms the already proposed approaches for unusual activity recognition. Apart from evaluating the proposed model on the examination dataset, we also apply AUAR on Violent and Movies datasets, widely used in the relevant literature to detect suspicious activities. The results reveal that AUAR performs well on various data sets compared to existing state-of-the-art models. activity detection, tracking and features extraction using CNN. The extracted features are then fed into Multi-class Support Vector Machine (MSVM) for classification and detection of suspicious activities. The experiments were performed on a standard dataset and achieved 95% accuracy. Jalal et al. proposed a method to recognize human interaction in an outdoor environment using a Multi-feature algorithm with CNN. The proposed method is evaluated on the BIT-Interaction dataset and recognize eight complex activities. The experimental results show 84.63% recognition accuracy. This article presents a novel deep learning-based unusual activity detection model in the examination hall. The proposed deep learning model is based on CNN. It outperforms existing models used for unusual activity recognition that uses computer vision and hardcoded algorithms to detect unusual activities during the examination. Apart from proposing the model, we have also developed a video dataset for unusual examination hall activities. The performance of the proposed research work is evaluated on a frame-level consisting of 11500 Key-frames, and the video level consists of 550 video clips of 4 different classes. We have used AUROC as an evaluation matrix. The detection results of deep learning models show excellent performance on our developed dataset. The accuracy of deep learning models for frame-level is higher than the video level due to limited video dataset and GPU resources. The proposed CNN models show an optimized accuracy on our unusual activity dataset regardless of dataset complexity and resource limitations. Apart from this examination dataset, we evaluated the proposed model on two other widely-used datasets, including the Violent-flow dataset and Movie dataset for unusual activity recognition. The proposed model outperformed existing models for all three datasets.