用自监督+多列CNN提升人群计数，结合时空模型检测异常行为。
Crowd Scene Analysis using Deep Learning Techniques
- 融合自监督训练与多列CNN，减少标注依赖并捕捉多尺度特征。
- 在ShanghaiTech和UCFQNRF上实现更低的MAE/MSE，优于现有方法。
- 适用于复杂场景下的人群监控，适合安防与智能城市应用。
本研究聚焦于人群场景分析的两个核心应用：人群计数与异常检测。针对深度学习模型依赖大量标注数据的问题，提出自监督训练策略以降低标注成本。通过结合自监督训练与多列卷积神经网络（MCNN），模型能有效学习不同层次的特征，在遮挡、密度不均、复杂背景及尺度变化等挑战下表现优异。该模型在ShanghaiTech和UCFQNRF公开数据集上以MAE和MSE为评估指标，性能领先。此外，基于VGG19构建的时空模型用于异常检测，利用CNN提取空间特征，LSTM捕捉时间动态，通过密集残差块替代全连接层提升性能。在Hockey Fight和SCVD数据集上的实验表明，该模型在正常/异常二分类任务中优于当前最优方法。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。