基于深度学习技术的拥挤场景分析
Crowd Scene Analysis using Deep Learning Techniques
AI总结：
本文针对拥挤场景分析中的计数与异常检测任务，提出结合自监督训练与多列CNN的计数模型以及基于VGG19和LSTM的时空异常检测模型，并在多个公开数据集上验证了其优于现有方法的性能。
摘要：
我们的研究聚焦于拥挤场景分析的两个主要应用：人群计数和异常检测。近年来，人群计数领域已经涌现出大量研究。我们解决了该领域的两个主要挑战：1）深度学习模型是数据饥渴型范式，训练算法总是需要大量标注数据。对如此大量的数据进行标注是一项耗时且成本高昂的任务。为此，提出了自监督训练方法来应对这一挑战。2）MCNN由具有不同滤波器尺寸的多列CNN组成，我们提出了一种基于自监督训练与多列CNN相结合的新方法。这使得模型能够在不同层级上学习特征，并使其能够有效应对遮挡场景、非均匀密度、复杂背景和尺度不变性等挑战。所提出的模型在公开数据集（如ShanghaiTech和UCF-QNRF）上通过MAE和MSE进行了评估。针对人群异常检测，提出了一种基于VGG19的时空模型，以应对光照、环境条件、意外物体和可扩展性等挑战。该模型提取空间和时间特征，使其能够泛化到真实世界场景。空间特征使用CNN学习，而时间特征使用LSTM块学习。该模型基于二分类工作，能够检测正常或异常行为。通过用密集残差块替换全连接层，模型的性能得到了提升。在Hockey Fight数据集和SCVD数据集上的实验表明，我们的模型优于其他最先进的方法。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
