Abnormal crowd behaviors in high density situations can pose great danger to public safety. Despite the extensive installation of closed-circuit television (CCTV) cameras, it is still difficult to achieve real-time alerts and automated responses from current systems. Two major breakthroughs have been reported in this research. Firstly, a spatial-temporal texture extraction algorithm is developed. This algorithm is able to effectively extract video textures with abundant crowd motion details. It is through adopting Gabor-filtered textures with the highest information entropy values. Secondly, a novel scheme for defining crowd motion patterns (signatures) is devised to identify abnormal behaviors in the crowd by employing an enhanced gray level co-occurrence matrix model. In the experiments, various classic classifiers are utilized to benchmark the performance of the proposed method. The results obtained exhibit detection and accuracy rates which are, overall, superior to other techniques.

目的针对现有高光谱与多光谱图像融合算法在光谱重建策略不足和模态融合机制缺失的问题，本文提出了一种稀疏重建与交叉引导的高光谱多光谱图像融合算法（Hyperspectral and Multispectral Image Fusion via Sparse Reconstruction and Cross-Guidance， HMIF-SRNet）。方法该算法采用端到端的融合架构，通过预处理、特征提取、核心融合和特征重建四个模块实现高质量融合。为了光谱信息的高效利用和精确保真以及双模态信息的充分互补，我们设计了两个核心模块：光谱稀疏重建单元（Spectral Reconstruction Unit，SRU）和交叉引导单元（Cross-Reference Unit，CRU）。具体来说，SRU模块采用双重统计门控策略和动态稀疏重建机制，自适应优化光谱维度的信息表达，有效降低光谱冗余；CRU模块通过交叉引导机制实现高光谱和多光谱特征的互补融合，增强了跨模态信息的交互效果。两个核心模块通过串行级联实现递进式的特征优化。结果在CAVE和Harvard两个标准数据集上与10种主流融合方法的对比实验表明，HMIF-SRNet在峰值信噪比（Peak Signal-to-Noise Ratio， PSNR）、相对全局无量纲误差（Relative Dimensionless Global Error in Synthesis，ERGAS）、光谱角映射（Spectral Angle Mapper，SAM）和通用图像质量指数（Universal Image Quality Index， UIQI）四个指标上均取得了最优结果，其中在CAVE数据集上PSNR相比次优方法提升了4.41dB，SAM减少了32.0%，在真实场景下展现出卓越的鲁棒性。结论通过轻量化网络架构设计，该算法在保持优异融合性能的同时有效降低了计算复杂度，验证了动态稀疏重建策略与交叉引导融合机制的有效性。