Online multi-object tracking (MOT) aims to maintain consistent target identities across video frames, yet it remains vulnerable to identity switches under occlusion and appearance variation. Many existing trackers rely on single-prototype exponential moving average (EMA) memory, which is efficient but prone to contamination, over-smoothing, and staleness. To address this issue, we propose Hierarchical Multi-Prototype Appearance Memory (HMP), a plug-and-play module for online MOT. HMP separates stable long-term identity anchors from short-term transitional evidence through a multi-prototype long-term memory and a short first-in-first-out (FIFO) queue. A unified joint reliability score governs memory writing and maintenance, and a frozen two-stage association strategy first performs stable primary matching and then allows conservative short-term recovery only on residual cases. Experiments on MOT17 and MOT20 show that HMP improves identity continuity while preserving competitive overall tracking quality. Controlled ablations further support the effectiveness of the proposed memory representation, reliability control, and staged evidence usage under fixed upstream modules.

In this paper we briefly present the concepts of ontology and Web Service, analyze the advantages that introduce them into robot control system. This research constructs the ontologies related with the control parameters of machine manipulator, designs a realization model and the corresponding services composition framework for the semantic Web Service based on ontology. Experimental results show that the approach improves the versatility of the various robot control systems, realizes the asynchronous communication within the clients and Web Service composites, thus enhances the real-time implementation of remote industrial robot control system as well as displays the information validity.

Model compression can effectively reduce deep learning models’ computational and storage demands, facilitating their deployment and inference at the edge, especially for image analysis tasks. However, investigating practical compression algorithms for multi-task models is still in its infancy. We propose a multi-task deep learning pruning method for disentangled networks based on the lottery ticket hypothesis (MTDLP). First, the method introduces a task importance metric for assigning weights to each independent task. It implements a consistent pruning strategy to ensure that the performance of critical tasks is not affected when redundant parameters are eliminated. Second, the method uses a parameter decoupling module to untangle the intertwining of task parameters by separating the critical parameters of each task through a parameter mask. Finally, the pruned sparse network undergoes weighted random reinitialization and retraining to recover model accuracy and optimize overall performance. We evaluate the MTDLP method on mainstream multi-task image analysis datasets, including NYU-v2 and Cityscapes. The experimental results demonstrate that the MTDLP method outperforms mainstream methods, such as single-shot network pruning and iterative magnitude pruning, while maintaining a high degree of model sparsity, highlighting its potential for efficient multi-task image analysis in resource-constrained environments.