CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos

Video Anomaly Detection (VAD) remains a fundamental yet formidable task in the video understanding community, with promising applications in areas such as information forensics and public safety protection. Due to the rarity and diversity of anomalies, existing methods only use easily collected regular events to model the inherent normality of normal spatial-temporal patterns in an unsupervised manner. Previous studies have shown that existing unsupervised VAD models are incapable of label-independent data offsets (e.g., scene changes) in real-world scenarios and may fail to respond to light anomalies due to the overgeneralization of deep neural networks. Inspired by causality learning, we argue that there exist causal factors that can adequately generalize the prototypical patterns of regular events and present significant deviations when anomalous instances occur. In this regard, we propose Causal Representation Consistency Learning (CRCL) to implicitly mine potential scene-robust causal variable in unsupervised video normality learning. Specifically, building on the structural causal models, we propose scene-debiasing learning and causality-inspired normality learning to strip away entangled scene bias in deep representations and learn causal video normality, respectively. Extensive experiments on benchmarks validate the superiority of our method over conventional deep representation learning. Moreover, ablation studies and extension validation show that the CRCL can cope with label-independent biases in multi-scene settings and maintain stable performance with only limited training data available.

A Polarization Image Dehazing Method Based on the Principle of Physical Diffusion

Computer vision is increasingly used in areas such as unmanned vehicles, surveillance systems and remote sensing. However, in foggy scenarios, image degradation leads to loss of target details, which seriously affects the accuracy and effectiveness of these vision tasks. Polarized light, due to the fact that its electromagnetic waves vibrate in a specific direction, is able to resist scattering and refraction effects in complex media more effectively compared to unpolarized light. As a result, polarized light has a greater ability to maintain its polarization characteristics in complex transmission media and under long-distance imaging conditions. This property makes polarized imaging especially suitable for complex scenes such as outdoor and underwater, especially in foggy environments, where higher quality images can be obtained. Based on this advantage, we propose an innovative semi-physical polarization dehazing method that does not rely on an external light source. The method simulates the diffusion process of fog and designs a diffusion kernel that corresponds to the image blurriness caused by this diffusion. By employing spatiotemporal Fourier transforms and deconvolution operations, the method recovers the state of fog droplets prior to diffusion and the light inversion distribution of objects. This approach effectively achieves dehazing and detail enhancement of the scene.