[1] multi-object tracking via discriminative embeddings for the internet of things. ieee internet of things journal. 2023, 10(12): 10532-10546. (sci中科院一区top期刊,if=10.238). 第一作者
[2] multi-object tracking via deep feature fusion and association analysis. engineering applications of artificial intelligence. 2023, (sci中科院一区,if= 7.802). 第一作者
[3] 基于双融合框架的多模态3d目标检测算法. 电子学报. 2023. (accepted, ei收录, ccf a类期刊). 通讯作者
[4] 基于全局自适应有向图的行人轨迹预测. 电子学报. 2022, 50(8): 1905-1916. (ei收录, ccf a类期刊). 通讯作者
[5] deep learning-based 3d multi-object tracking using multimodal fusion in smart cities. human-centric computing and information sciences. 2023. (accepted, sci中科院一区top期刊, if=6.558). 第一作者
[6] 图像与点云多重信息感知关联的三维多目标跟踪. 中国图象图形学报. 2023. (accepted, 图像图形领域t1级期刊). 通讯作者
[7] gsta: pedestrian trajectory prediction based on global spatio-temporal association of graph attention network. pattern recognition letters. 2022, 160: 90-97. (sci中科院三区, if=5.1). 通讯作者
[8] object detection method based on global feature augmentation and adaptive regression in iot. neural computing and applications. 2021, 33(9): 4119-4131. (sci中科院三区, if=6.0). 第一作者
[9] qos intelligent prediction for mobile video networks: a gr approach. neural computing and applications. 2021, 33(9): 3891-3900. (sci中科院三区, if=6.0). 通讯作者
[10] efficient and accurate object detection for 3d point clouds in intelligent visual internet of things. multimedia tools and applications. 2021, 80(20): 31297-31334. (sci收录). 第一作者
[11] pedestrian detection algorithm based on video sequences and laser point cloud [j]. frontiers of computer science. 2015, 9(3): 402-414 (sci 中科院二区,if=4.064). 第一作者
[12] dlfusion: painting-depth augmenting-lidar for multimodal fusion 3d object detection. acm mm. 2023. (accepted, ccf a类会议).
[13] 融合多尺度特征和多重注意力的水下目标检测[j]. 农业工程学报, 2022, 38(20):129-139. (ei期刊,中国科协农林领域 t1 级期刊). 第一作者
[14] 基于两阶段深度网络的输电线路异常目标检测方法[j]. 控制与决策. 2022, 37(7): 1873-1882. (ei期刊). 第一作者
[15] 基于时域扩张残差网络和双分支结构的人体行为识别[j]. 控制与决策. 2022, 37(11): 2993-3002(ei期刊). 通讯作者
[16] 基于多重信息融合与轨迹关联修正的多目标跟踪方法[j]. 控制与决策, 2023(ei期刊). 通讯作者