Among tasks related to intelligent interpretation of remote sensing data, scene classification mainly focuses on the holistic information of the entire scene. Compared to pixel-level or object-based tasks, it involves a richer semantic context, making it more challenging. With the rapid advancement of deep learning, convolutional neural networks (CNNs) have found widespread applications across various domains, and some work has introduced them into scene classification tasks. However, traditional convolution operations involve sliding small convolutional kernels across an image, primarily focusing on local details within a small receptive field. To achieve better modeling of the entire image, the smaller receptive field limits the ability of convolution operation to capture features over a broader range. To this end, we introduce a large kernel CNNs into the scene classification task to expand the receptive field of the mode, which allows us to capture comprehensive non-local information while still acquiring rich local details. However, in addition to encoding spatial association, the effective information within the feature maps is also strongly channel-related. Therefore, to fully model this channel dependency, a novel channel separation and mixing module has been designed to realize feature correlation in the channel dimension. The combination of them forms a Large Kernel Separable Mixed Convnet (LSMNet), enabling the model to capture effective dependencies of feature maps in both spatial and channel dimensions, thus achieving enhanced feature expression. Extensive experiments conducted on three datasets have also validated the effectiveness of the proposed method.

Multiple-object tracking (MOT) is a fundamental task in computer vision and is widely applied across various domains. However, its algorithms remain somewhat immature in practical applications. To address the challenges presented by complex scenarios featuring instances of missed detections, false alarms, and frequent target switching leading to tracking failures, we propose an approach to multi-object tracking utilizing KC-YOLO detection and an identity validity discrimination module. We have constructed the KC-YOLO detection model as the detector for the tracking task, optimized the selection of detection frames, and implemented adaptive feature refinement to effectively address issues such as incomplete pedestrian features caused by occlusion. Furthermore, we have introduced an identity validity discrimination module in the data association component of the tracker. This module leverages the occlusion ratio coefficient, denoted by “k”, to assess the validity of pedestrian identities in low-scoring detection frames following cascade matching. This approach not only enhances pedestrian tracking accuracy but also ensures the integrity of pedestrian identities. In experiments on the MOT16, MOT17, and MOT20 datasets, MOTA reached 75.9%, 78.5%, and 70.1%, and IDF1 reached 74.8%, 77.8%, and 72.4%. The experimental results demonstrate the superiority of the methodology. This research outcome has potential applications in security monitoring, including public safety and fire prevention, for tracking critical targets.

The motion vector similarity between neighboring blocks is widely used in motion estimation algorithms. However, for nonneighboring blocks, they may also have similar motions due to close depths or belonging to the same object inside the scene. Therefore, the motion vectors usually have several kinds of patterns, which reveal a clustering structure. In this article, we propose a progressive clustering algorithm, which periodically counts the motion vectors of the past blocks to make incremental clustering statistics. These statistics are used as the motion vector predictors for the following blocks. It is proved to be much more efficient for one block to find the best-matching candidate with the predictors. We also design the clustering based search with CUDA for GPU acceleration. Another interesting application of the clustering statistics is persistent static object tracking. Based on the statistics, several auxiliary tracking areas are created to guide the object tracking. Even when the target object has significant changes in appearance or it disappears occasionally, its position still can be predicted. The experiments on Xiph.org Video Test Media dataset illustrate that our clustering based search algorithm outperforms the mainstream and some state-of-the-art motion estimation algorithms. It is 33 times faster on average than the full search algorithm with only slightly higher mean-square error values in the experiments. The tracking results show that the auxiliary tracking areas help to locate the target object effectively.