Crowd counting has long been a crucial topic in the domains of computer vision and video surveillance. In particular, with the widespread use of thermal or depth cameras, RGB-X crowd counting has emerged as a prominent research focus. Although depth or infrared images provide complementary information, the core challenge remains in effectively unifying heterogeneous cross-modality and cross-scale information to form a comprehensive representation of crowd distributions in complex scenes. To address this problem, we propose a novel Mamba-based framework, termed UMS-VMamba-CC for multi-modal (RGB-X) crowd counting. Specifically, we design the cross-modality disentanglement fusion visual Mamba (CMDF-VMamba) that uses self-supervised learning to decompose modality-invariant and modality-specific features in spatial-frequency domains, followed by multi-modal feature aggregation via the gating mechanism. For cross-scale fusion, we design the cross-scale pyramid fusion visual Mamba (CSPF-VMamba), which adopts the bi-directional pyramid structure to incorporate low-level features into high-level representations during downsampling, while upsampling high-level features and aggregating them with low-level features through state space contextual modeling. Comprehensive experiments on multiple mainstream datasets demonstrate that the UMS-VMamba-CC framework achieves competitive performance for RGB-X crowd counting.

Recently, neural networks have become the dominant approach to low-light image enhancement (LLIE), with at least one-third of them adopting a Retinex-related architecture. However, through in-depth analysis, we contend that this most widely accepted LLIE structure is suboptimal, particularly when addressing the non-uniform illumination commonly observed in natural images. In this paper, we present a novel variant learning framework, termed residual quotient learning, to substantially alleviate this issue. Instead of following the existing Retinex-related decomposition-enhancement-reconstruction process, our basic idea is to explicitly reformulate the light enhancement task as adaptively predicting the latent quotient with reference to the original low-light input using a residual learning fashion. By leveraging the proposed residual quotient learning, we develop a lightweight yet effective network called ResQ-Net. This network features enhanced non-uniform illumination modeling capabilities, making it more suitable for real-world LLIE tasks. Moreover, due to its well-designed structure and reference-free loss function, ResQ-Net is flexible in training as it allows for zero-reference optimization, which further enhances the generalization and adaptability of our entire framework. Extensive experiments on various benchmark datasets demonstrate the merits and effectiveness of the proposed residual quotient learning, and our trained ResQ-Net outperforms state-of-the-art methods both qualitatively and quantitatively. Furthermore, a practical application in dark face detection is explored, and the preliminary results confirm the potential and feasibility of our method in real-world scenarios.

Driver drowsiness has currently been a severe issue threatening road safety, hence it is vital to develop an effective drowsiness recognition algorithm to avoid traffic accidents. However, recognizing drowsiness is still very challenging, due to the large intra-class variations in facial expression, head pose and illumination condition. In this paper, a new deep learning framework based on the hybrid of 3D conditional generative adversarial network and two-level attention bidirectional long short-term memory network (3DcGAN-TLABiLSTM) has been proposed for robust driver drowsiness recognition. Aiming at extracting short-term spatial-temporal features with abundant drowsiness-related information, we design a 3D encoder-decoder generator with the condition of auxiliary information to generate high-quality fake image sequences and devise a 3D discriminator to learn drowsiness-related representation from spatial-temporal domain. In addition, for long-term spatial-temporal fusion, we investigate the use of two-level attention mechanism to guide the bidirectional long short-term memory learn the saliency of short-term memory information and long-term temporal information. For experiment, we evaluate our 3DcGAN-TLABiLSTM framework on a public NTHU-DDD dataset. Experimental results show that the proposed approach achieves higher precision of drowsiness recognition compared to the state-of-the-art.

Abnormal driving behaviour is one of the leading cause of terrible traffic accidents endangering human life. Therefore, study on driving behaviour surveillance has become essential to traffic security and public management. In this paper, we conduct this promising research and employ a two stream CNN framework for video-based driving behaviour recognition, in which spatial stream CNN captures appearance information from still frames, whilst temporal stream CNN captures motion information with pre-computed optical flow displacement between a few adjacent video frames. We investigate different spatial-temporal fusion strategies to combine the intra frame static clues and inter frame dynamic clues for final behaviour recognition. So as to validate the effectiveness of the designed spatial-temporal deep learning based model, we create a simulated driving behaviour dataset, containing 1237 videos with 6 different driving behavior for recognition. Experiment result shows that our proposed method obtains noticeable performance improvements compared to the existing methods.