一种基于计算机视觉的利用CNN-LSTM-MLP混合融合模型进行跟踪（stalking）检测的方法
A Computer Vision Based Approach for Stalking Detection Using a CNN-LSTM-MLP Hybrid Fusion Model
AI总结：
提出一种基于CNN-LSTM-MLP混合融合模型的计算机视觉方法，从最少视频帧中提取面部、姿态和距离特征以检测跟踪行为，在自建数据集上达到89.58%的测试准确率。
摘要：
犯罪和可疑活动检测近年来已成为一个热门研究课题。计算机视觉技术的快速发展对解决这一问题产生了至关重要的影响。然而，尽管现代技术不断进步，物理跟踪（stalking）检测仍是一个探索较少的领域。如今，在公共场所发生的跟踪行为已变得常见，其中女性受影响最为严重。跟踪是一种可见的行为，通常发生在任何犯罪活动开始之前，因为跟踪者会在实施攻击、绑架、强奸等犯罪活动之前，先开始跟随、徘徊和注视受害者。因此，通过检测跟踪行为来预防这些犯罪活动已成为当务之急，因为所有这些犯罪活动都可以通过跟踪检测在第一时间被阻止。在本研究中，我们提出了一种新颖的基于深度学习的混合融合模型，旨在从包含最少帧数的单个视频中检测潜在的跟踪者。我们从视频帧中提取多种相关特征，如面部关键点、头部姿态估计和相对距离，并将其作为数值数据。这些数据被输入到一个多层感知器（MLP）中，以执行跟踪场景与非跟踪场景之间的分类任务。同时，视频帧被输入到卷积神经网络（CNN）和长短期记忆网络（LSTM）模型的组合中，以提取时空特征。我们融合这些数值特征和时空特征来构建一个分类器，用于检测跟踪事件。此外，我们引入了一个由从各种故事片和电视剧中收集的跟踪和非跟踪视频组成的数据集，该数据集也用于训练模型。实验结果表明，我们提出的跟踪者检测系统具有高效性和动态性，其测试准确率达到了89.58%，与最先进的方法相比有显著提升。
英文摘要：
Criminal and suspicious activity detection has become a popular research topic in recent years. The rapid growth of computer vision technologies has had a crucial impact on solving this issue. However, physical stalking detection is still a less explored area despite the evolution of modern technology. Nowadays, stalking in public places has become a common occurrence with women being the most affected. Stalking is a visible action that usually occurs before any criminal activity begins as the stalker begins to follow, loiter, and stare at the victim before committing any criminal activity such as assault, kidnapping, rape, and so on. Therefore, it has become a necessity to detect stalking as all of these criminal activities can be stopped in the first place through stalking detection. In this research, we propose a novel deep learning-based hybrid fusion model to detect potential stalkers from a single video with a minimal number of frames. We extract multiple relevant features, such as facial landmarks, head pose estimation, and relative distance, as numerical values from video frames. This data is fed into a multilayer perceptron (MLP) to perform a classification task between a stalking and a non-stalking scenario. Simultaneously, the video frames are fed into a combination of convolutional and LSTM models to extract the spatio-temporal features. We use a fusion of these numerical and spatio-temporal features to build a classifier to detect stalking incidents. Additionally, we introduce a dataset consisting of stalking and non-stalking videos gathered from various feature films and television series, which is also used to train the model. The experimental results show the efficiency and dynamism of our proposed stalker detection system, achieving 89.58% testing accuracy with a significant improvement as compared to the state-of-the-art approaches.
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