A Spatial-temporal Collaborative Optimization Method for Stable Grab Trajectory Extraction

CHEN Xiaoyu; ZHANG Fengzhuo; CHEN Yang; LIU Wenyuan; KONG Deming

doi:10.11999/JEIT260512

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CHEN Xiaoyu, ZHANG Fengzhuo, CHEN Yang, LIU Wenyuan, KONG Deming. A Spatial-temporal Collaborative Optimization Method for Stable Grab Trajectory Extraction[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260512

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CHEN Xiaoyu, ZHANG Fengzhuo, CHEN Yang, LIU Wenyuan, KONG Deming. A Spatial-temporal Collaborative Optimization Method for Stable Grab Trajectory Extraction[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260512

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A Spatial-temporal Collaborative Optimization Method for Stable Grab Trajectory Extraction

doi: 10.11999/JEIT260512 cstr: 32379.14.JEIT260512

1.
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2.
Dalian Huarui Intelligent Technology Co., Ltd., Dalian 116013, China
3.
School of Artificial Intelligence, Yanshan University, Qinhuangdao 066004, China
4.
School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
5.
Hebei Key Laboratory of Measurement Technology and Instrumentation, Yanshan University, Qinhuangdao 066000, Hebei Province, China

Funds: The Natural Science Foundation of Hebei Province (F2025203055)

Received Date: 2026-04-27
Accepted Date: 2026-07-01
Rev Recd Date: 2026-07-01

Available Online: 2026-07-23

Abstract

Abstract

Objective In port operation videos, the grab is a continuously moving target, and accurate trajectory extraction is essential for operation monitoring, equipment coordination, and collision warning. However, complex backgrounds, scale variations, partial occlusion, and boundary degradation often reduce the stability of target region segmentation, leading to centroid deviation, trajectory jitter, missed detections, and trajectory discontinuity. To address these challenges, a Spatial-Temporal Collaborative Optimization Method is proposed for stable and continuous grab trajectory extraction. While maintaining high inference speed, the proposed method improves both trajectory extraction accuracy and trajectory stability, providing a practical solution for stable perception of continuously moving targets in port industrial video scenarios. Methods Built on YOLOv8-seg, the proposed framework integrates Spatial Representation Enhancement (SRE) and Temporal CONSistency constraint (TCONS). First, CBAM, BiFPN-lite, and shallow feature aggregation are incorporated to improve target-background separability, enhance multi-scale feature representation, and preserve boundary details. TCONS is then imposed on prototype features through global average pooling, a cache-based pairing mechanism, and a weighted Charbonnier loss to suppress the temporal accumulation of local errors. In addition, a stage-wise training strategy with warm-up epochs and a joint optimization objective is adopted to ensure stable convergence. Results and Discussions Experiments are conducted on DAVIS2016, SegTrackV2, and a real portal crane grab dataset to evaluate the proposed method in terms of segmentation performance, trajectory stability, and occlusion robustness. The proposed method achieves the best segmentation performance on the real portal crane grab dataset, with J and F scores of 90.05% and 98.56%, respectively. It also improves performance on DAVIS2016 while maintaining comparable performance with slight gains on SegTrackV2 (Tables 1 and 2, Fig. 2). In terms of trajectory stability, compared with YOLOv8-seg, the proposed method reduces MAE and RMSE by approximately 55.3% and 52.6%, respectively, and decreases the miss rate to 0.56% (Table 5). It also produces a more concentrated trajectory error distribution and a smaller fluctuation range (Fig. 3). Occlusion robustness experiments further demonstrate that, under different occlusion ratios, the proposed method maintains good region integrity and continuous target extraction capability, reducing the maximum number of consecutive missed frames from 52 to 47 (Table 6, Figs. 4 and 5). Ablation studies verify the complementary effects of SRE and TCONS, whereas parameter analysis shows that a TCONS weight of 0.3 provides the best balance between segmentation quality and trajectory stability (Tables 7 and 8). Conclusions A Spatial-Temporal Collaborative Optimization Method is proposed to address the challenge of stable grab trajectory extraction in port operation videos. Experimental results demonstrate that the proposed method achieves high segmentation accuracy and stable trajectory extraction on DAVIS2016 and the real portal crane grab dataset, while maintaining comparable segmentation performance on SegTrackV2. It also exhibits strong continuous target extraction capability under occlusion without significantly sacrificing inference speed. Since the current study is limited to fixed crane viewpoints, future work will focus on cross-scene generalization and long-term continuous perception under more complex operating conditions to further improve the robustness and applicability of the proposed method in real-world environments.

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References(26)

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