Video Camouflaged Object Detection (VCOD) poses significant challenges due to the subtle appearance of camouflaged objects, especially under dynamic motion and occlusion. Existing methods predominantly rely on optical flow or black-box features for motion modeling, which often entail substantial computational costs and suffer from limited interpretability. Inspired by the human strategy of identifying abnormal movements between frames and the principle of event camera image formation, we propose an eventstream-inspired dual-branch framework for VCOD. Specifically, we design an eventstream-like data extraction module to capture pixel-level motion variations, effectively distinguishing object motion from background dynamics. This event-based representation is integrated into SAM2 through a dual-branch memory-augmented framework, consisting of Time Bridge Attention and Visual Bridge Attention, enabling joint modeling of motion and appearance cues. In addition, we introduce a Prompt Embedding Generator to eliminate the need for human-provided interactive prompts, facilitating fully automatic VCOD. Extensive experiments on MoCA-Mask and CAD2016 demonstrate that our approach significantly outperforms state-of-the-art methods, achieving both superior segmentation accuracy and interpretable motion modeling. To the best of our knowledge, this is the first work to incorporate eventstream-inspired representations into the VCOD task.

According to Darwinian evolutionary theory, numerous species in the wild have developed remarkable adaptive mechanisms, involving pattern rearrangement and environmental assimilation, to evade predators. These obfuscation strategies pose significant challenges for both individuals and algorithms when performing the Camouflage Object Detection (COD) task in complex and intricate scenarios. Inspired by human strategies in the COD task, which involve assigning uncertainties to the entire input and then focusing on highly uncertain areas with the aid of prior knowledge such as boundary information, we propose the Uncertainty-Edge Dual Guide (UEDG) architecture. UEDG effectively combines probabilistic-derived uncertainty and deterministic-derived edge information to accurately detect concealed objects. The architecture consists of two independent branches dedicated to uncertainty reasoning and edge inference, which are subsequently integrated into a feature fusion module utilizing recursion feedback and feature-reuse techniques. This novel COD framework leverages the benefits of Bayesian learning and convolution-based learning, resulting in a powerful multi-task guided approach. Extensive experiments conducted on four widely employed datasets demonstrate the superior performance of UEDG compared to 12 state-of-the-art approaches, while maintaining an acceptable level of computational complexity. Overall, UEDG presents a promising solution for addressing the challenges of COD in complex environments by combining evolutionary-inspired strategies with advanced computer vision techniques.

Object detection is a fundamental task for autonomous driving, which aim to identify and localize objects within an image. Deep learning has made great strides for object detection, with popular models including Faster R-CNN, YOLO, and SSD. The detection accuracy and computational cost of object detection depend on the spatial resolution of an image, which may be constrained by both the camera and storage considerations. Furthermore, original images are often compressed and uploaded to a remote server for object detection. Compression is often achieved by reducing either spatial or amplitude resolution or, at times, both, both of which have well-known effects on performance. Detection accuracy also depends on the distance of the object of interest from the camera. Our work examines the impact of spatial and amplitude resolution, as well as object distance, on object detection accuracy and computational cost. As existing models are optimized for uncompressed (or lightly compressed) images over a narrow range of spatial resolution, we develop a resolution-adaptive variant of YOLOv5 (RA-YOLO), which varies the number of scales in the feature pyramid and detection head based on the spatial resolution of the input image. To train and evaluate this new method, we created a dataset of images with diverse spatial and amplitude resolutions by combining images from the TJU and Eurocity datasets and generating different resolutions by applying spatial resizing and compression. We first show that RA-YOLO achieves a good trade-off between detection accuracy and inference time over a large range of spatial resolutions. We then evaluate the impact of spatial and amplitude resolutions on object detection accuracy using the proposed RA-YOLO model. We demonstrate that the optimal spatial resolution that leads to the highest detection accuracy depends on the ‘tolerated’ image size (constrained by the available bandwidth or storage). We further assess the impact of the distance of an object to the camera on the detection accuracy and show that higher spatial resolution enables a greater detection range. These results provide important guidelines for choosing the image spatial resolution and compression settings predicated on available bandwidth, storage, desired inference time, and/or desired detection range, in practical applications.