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Modeling spatial–temporal constraints and spatial-Transfer Patterns for Couriers’ Package Pick-up Route Prediction

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第12期24卷 13787-13800页

作者： Wen, Haomin Lin, Youfang Hu, Yuxuan Wu, Fan Xia, Mingxuan Zhang, Xinyi Wu, Lixia Hu, Haoyuan Wan, Huaiyu Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China Cainiao Network Hangzhou 311100 Peoples R China

Couriers' package pick-up route prediction is a fundamental task in the emerging intelligent logistics systems. It is beneficial for order dispatching and arrival-time estimation by leveraging the predicted routes to improve those downstream tasks. However, the package pick-up route prediction problem is challenging since couriers' behaviors are affected by both strict spatial-temporal constraints (STC) and personalized spatial-transfer patterns (STP). Specifically, couriers have to consider explicitly spatial-temporal requirements such as the locations of packages and the promised pick-up time when selecting future routes. In addition, couriers have personalized mobility patterns between different locations, which are implicit patterns hidden behind couriers' historical trajectories and cannot be ignored to precisely depict their behaviors. This paper proposes a novel framework, named CP-Route, to predict a specific courier's future package pick-up route under strict spatial-temporal constraints, and enhance the prediction performance by learning the spatial-transfer patterns (i.e., the routing patterns) of couriers. A sophisticated encoder is designed to capture the STC and STP, and a mixed-distribution-based decoder is designed to simultaneously consider the influence of spatial-temporal constraints and routing patterns on couriers' final decisions. Extensive experiments conducted on an industry-scale logistics dataset demonstrate the superiority of our proposed framework against the existing baseline methods. The online A/B test shows our contribution to the improvement of arrival time prediction.

关键词： Package pick-up route prediction decision making process spatial-temporal constraints routing patterns

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Correlation Tracking via spatial-temporal constraints and Structured Sparse Regularization

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IEEE ACCESS 2021年 9卷 82675-82685页

作者： Tian, Dan Zang, Shouyu Tu, Binbin Shenyang Univ Sch Informat Engn Shenyang 110044 Peoples R China

Discriminative correlation filter (DCF) has achieved promising performance in visual tracking for its high efficiency and high accuracy. However, DCF trackers usually suffer from some challenges, such as boundary effects and appearance changes. In this paper, we propose a novel correlation tracking method via spatial-temporal constraints and structured sparse regularization. Firstly, we introduce the background-aware selection strategy to extract real negative examples, and penalize the filter coefficients close to the boundary locations for spatial protection, both of which can alleviate the boundary effects. Secondly, we restrict the filters with structured sparse regularization to handle the local appearance changes, and exploit temporal consistent constraint on the filters to address the global appearance changes. Finally, we employ the alternative direction method of multipliers to optimize our correlation tracking model. In our optimization framework, we combine grayscale, color names, histogram of orientation gradient with deep features for appearance learning to improve the discrimination. Meanwhile, we penalize spatial constraint and structured sparse regularization alternatively based on occlusion detection to enhance processing efficiency. The qualitative and quantitative experiments are conducted on the OTB dataset. Experimental results demonstrate that the proposed tracker has better performance than other state-of-the-art trackers.

关键词： Correlation Target tracking Information filters Visualization Strain Optimization Lighting Object tracking correlation filter spatial-temporal constraints structured sparse regularization deep feature

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temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing spatial-temporal constraints for Heterogeneous Grasslands

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REMOTE SENSING 2017年第9期9卷 968-968页

作者： Ding, Chao Liu, Xiangnan Huang, Fang China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China Northeast Normal Univ Sch Geog Sci Changchun 130024 Jilin Peoples R China

Continuous satellite-derived leaf area index (LAI) time series are critical for modeling land surface process. In this study, we present an interpolation algorithm to predict the missing data in LAI time series for ecosystems with high within-ecosystem heterogeneity, particularly heterogeneous grasslands. The algorithm is based on spatial-temporal constraints, i.e., the missing data in the LAI time series of a pixel are predicted by the phenological links with other pixels. To address the uncertainties in the construction and selection of reference curves in a heterogeneous landscape, the algorithm constructs a reference dataset for each missing data in the LAI time series from all pixels showing very strong linear phenological links with the target pixel within a region. We also use an iterative process to update the available spatial-temporal constraints. We tested the algorithm with an eight-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product in the Songnen grasslands, Northeast China in 2010 and 2011. The validation dataset was generated based on high quality time series by artificially adding data gaps. The algorithm achieved high overall interpolation accuracies with high coefficient of determination R-2 (>0.9) and low root mean square error (RMSE) (<0.2) in both dry (2010) and wet (2011) years. The algorithm showed advantages in predicting missing data for different seasons and proportions of missing data versus the algorithm that uses regional average LAI curve as a reference. These results suggest that the proposed algorithm could more effectively characterize spatial-temporal constraint information in heterogeneous grasslands for temporal interpolation.

关键词： leaf area index time series MODIS temporal interpolation spatial-temporal constraints vegetation phenology heterogeneous landscape semi-arid grasslands

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Online decentralized information gathering with spatial-temporal constraints

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AUTONOMOUS ROBOTS 2014年第1期37卷 1-25页

作者： Gan, Seng Keat Fitch, Robert Sukkarieh, Salah Univ Sydney Australian Ctr Field Robot Darlington NSW 2006 Australia

We are interested in coordinating a team of autonomous mobile sensor agents in performing a cooperative information gathering task while satisfying mission-critical spatial-temporal constraints. In particular, we present a novel set of constraint formulations that address inter-agent collisions, collisions with static obstacles, network connectivity maintenance, and temporal-coverage in a resource-efficient manner. These constraints are considered in the context of the target search problem, where the team plans trajectories that maximize the probability of target detection. We model constraints continuously along the agents' trajectories and integrate these constraint models into decentralized team planning using a computationally efficient solution method based on the Lagrangian formulation and decentralized optimization. We validate our approach in simulation with five UAVs performing search, and through hardware experiments with four indoor mobile robots. Our results demonstrate team planning with spatial-temporal constraints that preserves the performance of unconstrained information gathering and is feasible to implement with reasonable computational and communication resources.

关键词： Team planning Decentralized optimization Information gathering spatial-temporal constraints Collision avoidance Network connectivity temporal-coverage

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PTB: A deep reinforcement learning method for flexible logistics service combination problem with spatial-temporal constraint

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 195卷

作者： Tian, Ran Chang, Longlong Sun, Zhihui Zhao, Guanglu Lu, Xin Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

The Fourth Party Logistics Service platform aims to provide customers with one-stop logistics services by combining logistics services from different logistics service providers. However, it is an important challenge to quickly build the logistics service combination with the maximum overall profit under the premise of satisfying spatial-temporal constraints. The traditional heuristic algorithm has the problem of low computing efficiency and insufficient adaptability to dynamic environment and demand uncertainty when dealing with the problem of large-scale logistics service combination with spatial-temporal constraints and uncertainty of service. Therefore, we propose the Proximal policy optimization algorithm integrating the Transformer network and the Bayesian networks (PTB). Firstly, a variable length sequence module based on the greedy strategy is used in the environment to generate the status information of the order, to solve the problem that the traditional reinforcement learning environment cannot handle any number of service providers. Then, a policy network integrating the Transformer and Bayes network is used to further optimize the accuracy and reliability of scheduling decisions. Meanwhile the PPO algorithm is used to update the network and constantly optimize the scheduling decisions of agents. Finally, the experimental results on four sets of logistics order scenes show that PTB can effectively solve the logistics service combination problem with spatial-temporal constraints, and the results of most logistics service combination tasks are better than other baseline models, and in large-scale order scenes, it has far more computational efficiency than the traditional heuristic algorithm, and shows good generalization ability.

关键词： spatial-temporal constraints Logistics service combination Deep reinforcement learning Transformer Bayesian network

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Online Multi-camera People Tracking with spatial-temporal Mechanism and Anchor-feature Hierarchical Clustering

Online Multi-camera People Tracking with Spatial-temporal Me...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Cherdchusakulchai, Riu Phimsiri, Sasin Trairattanapa, Visarut Tungjitnob, Suchat Kudisthalert, Wasu Kiawjak, Pornprom Thamwiwatthana, Ek Borisuitsawat, Phawat Tosawadi, Teepakorn Choppradit, Pakcheera Mahakijdechachai, Kasisdis Vatathanavaro, Supawit Saetan, Worawit Suttichaya, Vasin AI & Robot Ventures ARV Bangkok Thailand

ISBN: (纸本)9798350365474

Multi-camera Multi-object tracking (MTMC) surpasses conventional single-camera tracking by enabling seamless object tracking across multiple camera views. This capability is critical for security systems and improving situational awareness in various environments. This paper proposes a novel MTMC framework designed for online operation. The framework employs a three-stage pipeline: Multiobject Tracking (MOT), Multi-target Multi-camera Tracking (MTMC), and Cross Interval Synchronization (CIS). In the MOT stage, ReID features are extracted and localized tracklets are created. MTMC links these tracklets across cameras using spatial-temporal constraints and constraint hierarchical clustering with anchor features for improved inter-camera association. Finally, CIS ensures the temporal coherence of tracklets across time intervals. The proposed framework achieves robust tracking performance, validated on the challenging 2024 AI City Challenge with a HOTA score of 51.0556%, ranking sixth. The code is available at: https://***/ARV-MLCORE/AIC2024 Track1 ARV

关键词： Hierarchical Clustering Multi-object Tracking Online Multi-camera Multi-object tracking Re-identification spatial-temporal constraints

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Constrained Gaussian Condensation Filter for Cooperative Target Tracking

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IEEE INTERNET OF THINGS JOURNAL 2022年第3期9卷 1861-1874页

作者： Xu, Cheng Wu, Hang Duan, Shihong Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Grad Sch Foshan 528300 Peoples R China Beijing Lab Knowledge Engn Mat Sci Beijing 100083 Peoples R China

Real-time high-precision navigation has many applications, such as pedestrian navigation, emergency rescue, and vehicle networks. In practice, the measurement models are often nonlinear, and sequential Bayesian filters, such as Kalman and particle filter, suffer from accumulative errors, which cannot provide long-time high-precision services for localization. To solve arbitrary noise distribution, this article proposes a Gaussian condensation filter (GCF) algorithm to achieve high-precision localization in a non-Gaussian noise environment. To this end, we proposed an error-ellipse resampling (EER)-based GCF (EER-GCF), which establishes error ellipses with different confidence probabilities and implements a resampling algorithm based on the sampling points' geometrical positions. Furthermore, a cooperative EER-based GCF (CEER-GCF) is proposed to enhance information fusion in the multitarget network. This study accomplishes cooperative tracking based on spatial-temporal constraints to enhance error correction. The experimental results show that CEER-GCF can effectively eliminate the accumulative error and optimize state estimation, which outperforms state of the arts, such as unscented Kalman filter and particle filter.

关键词： Target tracking Distance measurement Kalman filters Time measurement State estimation Optimization Measurement uncertainty Cooperative tracking error-ellipse resampling (EER) Gaussian mixture distribution location aware spatial-temporal constraints

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Mutuality-oriented reconstruction and prediction hybrid network for video anomaly detection

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SIGNAL IMAGE AND VIDEO PROCESSING 2022年第7期16卷 1747-1754页

作者： Wang, Wenqian Chang, Faliang Liu, Chunsheng Shandong Univ Sch Control Sci & Engn 17923 Jingshi Rd Jinan 250061 Peoples R China

Anomaly detection in surveillance videos aiming to locate anomalous events is a very challenging task, since when training a detector, only normal video samples are available. And thus, most existing approaches address this problem in a semi-supervised way by either predicting or reconstructing the video frames and then compute anomaly scores by comparing the generated frames with reference frames. However, reconstruction-based methods usually lead to mis-detection due to the excessively powerful reconstruction abilities yet the incapable capturing of temporal information, while prediction-based methods are able to perceive temporal information but insufficient to produce realistic future frames. To overcome these problems, we propose a novel Mutuality-Oriented Reconstruction and Prediction Hybrid Network (MORPH-Net) for detecting anomalous events. In the MORPH-Net, a new Mutuality-oriented Training (MO-Training) mechanism is introduced to better combine the advantages of prediction-based models and reconstruction-based models. Compared to traditional single training mechanism or simple fusion mechanism, the MO-Training mechanism can prompt the generator module to produce temporally discriminative and realistic frames which benefit the anomaly detection. The experiments evaluated on three large-scale video surveillance datasets show the efficacy of our method compared with the state-of-the-art approaches.

关键词： Anomaly detection Video prediction and reconstruction spatial-temporal constraints Generative adversarial network

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Traffic Data Recovery From Corrupted and Incomplete Observations via spatial-temporal TRPCA

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第10期23卷 17835-17848页

作者： Feng, Xinxin Zhang, Haitao Wang, Can Zheng, Haifeng Fuzhou Univ Coll Phys & Informat Engn Fujian Key Lab Intelligent Proc & Wireless Transm Fuzhou 350108 Peoples R China

Traffic information can be used for real-time traffic management and long-term transportation planning to increase traffic efficiency and safety. However, data containing both missing and deviating values, can seriously affect the accuracy of traffic information, even leading to incorrect results in traffic data analysis. In this paper, we propose a novel tensor-based data recovery method named spatial-temporal tensor robust principal component analysis (ST-TRPCA) to recover traffic data from corrupted and incomplete observations. Specifically, we not only fully account for the spatial-temporal properties of traffic data to increase the data recovery accuracy, but also utilize tensor factorization and its low-dimensional representation to improve computational efficiency. The extensive experimental results performed on real-world traffic dataset under various scenarios show that ST-TRPCA outperforms other state-of-the-art methods in both missing data recovery and anomaly detection, especially when the traffic data are severely corrupted.

关键词： Tensors Anomaly detection Data models Principal component analysis Roads Discrete Fourier transforms Mathematical models Tensor robust PCA tensor completion spatial-temporal constraints anomaly detection

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C3P: Cross-Domain Pose Prior Propagation for Weakly Supervised 3D Human Pose Estimation 17th

C3P: Cross-Domain Pose Prior Propagation for Weakly Supervis...

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17th European Conference on Computer Vision (ECCV)

作者： Wu, Cunlin Xiao, Yang Zhang, Boshen Zhang, Mingyang Cao, Zhiguo Zhou, Joey Tianyi Huazhong Univ Sci & Technol Sch AIA Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China Tencent YouTu Lab Shanghai Peoples R China ASTAR Ctr Frontier AI Res CFAR Singapore Singapore

ISBN: (纸本)9783031200649;9783031200656

This paper first proposes and solves weakly supervised 3D human pose estimation (HPE) problem in point cloud, via propagating the pose prior within unlabelled RGB-point cloud sequence to 3D domain. Our approach termed C3P does not require any labor-consuming 3D keypoint annotation for training. To this end, we propose to transfer 2D HPE annotation information within the existing large-scale RGB datasets (e.g., MS COCO) to 3D task, using unlabelled RGB-point cloud sequence easy to acquire for linking 2D and 3D domains. The self-supervised 3D HPE clues within point cloud sequence are also exploited, concerning spatial-temporal constraints on human body symmetry, skeleton length and joints' motion. And, a refined point set network structure for weakly supervised 3D HPE is proposed in encoder-decoder manner. The experiments on CMU Panoptic and ITOP datasets demonstrate that, our method can achieve the comparable results to the 3D fully supervised state-of-the-art counterparts. When large-scale unlabelled data (e.g., NTU RGB+D 60) is used, our approach can even outperform them under the more challenging cross-setup test setting.

关键词： 3D human pose estimation Weak supervision RGB-point cloud sequence spatial-temporal constraints

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