Crowd Scene Analysis using Deep Learning Techniques

• URL: http://arxiv.org/abs/2505.08834v2
• Date: Wed, 04 Jun 2025 03:58:31 GMT
• Title: Crowd Scene Analysis using Deep Learning Techniques
• Authors: Muhammad Junaid Asif,
• Abstract summary: Our research is focused on two main applications of crowd scene analysis.<n>Deep learning models are datahungry and always need a large amount of annotated data for the training of algorithm.<n>Atemporal model based on VGG19 is proposed for crowd anomaly detection.<n>Model works on binary classification and can detect normal or abnormal behavior.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Our research is focused on two main applications of crowd scene analysis crowd counting and anomaly detection In recent years a large number of researches have been presented in the domain of crowd counting We addressed two main challenges in this domain 1 Deep learning models are datahungry paradigms and always need a large amount of annotated data for the training of algorithm It is timeconsuming and costly task to annotate such large amount of data Selfsupervised training is proposed to deal with this challenge 2 MCNN consists of multicolumns of CNN with different sizes of filters by presenting a novel approach based on a combination of selfsupervised training and MultiColumn CNN This enables the model to learn features at different levels and makes it effective in dealing with challenges of occluded scenes nonuniform density complex backgrounds and scale invariation The proposed model was evaluated on publicly available data sets such as ShanghaiTech and UCFQNRF by means of MAE and MSE A spatiotemporal model based on VGG19 is proposed for crowd anomaly detection addressing challenges like lighting environmental conditions unexpected objects and scalability The model extracts spatial and temporal features allowing it to be generalized to realworld scenes Spatial features are learned using CNN while temporal features are learned using LSTM blocks The model works on binary classification and can detect normal or abnormal behavior The models performance is improved by replacing fully connected layers with dense residual blocks Experiments on the Hockey Fight dataset and SCVD dataset show our models outperform other stateoftheart approaches

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