双向跳帧预测用于视频异常检测的域内差异驱动注意力
Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention
AI总结：
本文提出BiSP网络，通过双向跳帧预测和域内差异驱动注意力，提升视频异常检测的判别能力。
摘要：
随着视频监控设备的广泛应用和智能系统开发的需求，视频异常检测（VAD）已成为构建智能监控系统的重要组成部分。扩大正常与异常事件之间的判别边界以提高性能是VAD的共同目标和挑战。为了解决这一问题，我们提出了一种基于双流自编码器的双向跳帧预测（BiSP）网络，从学习不同特征之间的域内差异的角度出发。BiSP在训练阶段跳过帧以分别实现前向和后向帧预测，而在测试阶段利用双向连续帧共同预测相同的中间帧，从而扩大正常与异常事件之间的差异程度。BiSP从运动模式和物体尺度的角度分别设计了方差通道注意力和上下文空间注意力，从而确保在不同维度下特征提取和传输过程中最大化正常与异常之间的差异。来自四个基准数据集的广泛实验验证了所提出BiSP的有效性，其显著优于最先进的竞争方法。
英文摘要：
With the widespread deployment of video surveillance devices and the demand for intelligent system development, video anomaly detection (VAD) has become an important part of constructing intelligent surveillance systems. Expanding the discriminative boundary between normal and abnormal events to enhance performance is the common goal and challenge of VAD. To address this problem, we propose a Bidirectional Skip-frame Prediction (BiSP) network based on a dual-stream autoencoder, from the perspective of learning the intra-domain disparity between different features. The BiSP skips frames in the training phase to achieve the forward and backward frame prediction respectively, and in the testing phase, it utilizes bidirectional consecutive frames to co-predict the same intermediate frames, thus expanding the degree of disparity between normal and abnormal events. The BiSP designs the variance channel attention and context spatial attention from the perspectives of movement patterns and object scales, respectively, thus ensuring the maximization of the disparity between normal and abnormal in the feature extraction and delivery with different dimensions. Extensive experiments from four benchmark datasets demonstrate the effectiveness of the proposed BiSP, which substantially outperforms state-of-the-art competing methods.
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。
