Online Multi-Object Tracking (MOT) plays a pivotal role in autonomous systems. The state-of-the-art approaches usually employ a tracking-by-detection method, and data association plays a critical role. This paper proposes a learning and graph-optimized (LEGO) modular tracker to improve data association performance in the existing literature. The proposed LEGO tracker integrates graph optimization, which efficiently formulates the association score map, facilitating the accurate and efficient matching of objects across time frames. To further enhance the state update process, the Kalman filter is added to ensure consistent tracking by incorporating temporal coherence in the object states to further enhance the state update process. Our proposed method, utilising LiDAR alone, has shown exceptional performance compared to other online tracking approaches, including LiDAR-based and LiDAR-camera fusion-based methods. LEGO ranked 3rdamong all trackers (both online and offline) and 2ndamong all online trackers in the KITTI MOT benchmark for cars1, at the time of submitting results to KITTI object tracking evaluation ranking board. Moreover, our method also achieves competitive performance on the Waymo open dataset benchmark.

License plate (LP) recognition is crucial in intelligent traffic management systems. However, factors such as long distances and poor camera quality often lead to severe degradation of captured LP images, posing challenges to accurate recognition. The design of License Plate Image Restoration (LPIR) methods frequently relies on synthetic degraded data, which limits their effectiveness on real-world severely degraded LP images. To address this issue, we introduce the first paired LPIR dataset collected in real-world scenarios, named MDLP, including 10,245 pairs of multi-frame severely degraded LP images and their corresponding clear images. To better restore severely degraded LP, we propose a novel Diffusion-based network, called LP-Diff, to tackle real-world LPIR tasks. Our approach incorporates (1) an Inter-frame Cross Attention Module to fuse temporal information across multiple frames, (2) a Texture Enhancement Module to restore texture information in degraded images, and (3) a Dual-Pathway Fusion Module to select effective features from both channel and spatial dimensions. Extensive experiments demonstrate the reliability of our dataset for model training and evaluation. Our proposed LP-Diff consistently outperforms other state-of-the-art image restoration methods on real-world LPIR tasks. The dataset and code are available at https://github.com/haoyGONG/LP-Diff.

Spatio-temporal sequence forecasting is one of the fundamental tasks in spatio-temporal data mining.It facilitates many real world applications such as precipitation nowcasting, citywide crowd flow prediction and air pollution forecasting.Recently, a few Seq2Seq based approaches have been proposed, but one of the drawbacks of Seq2Seq models is that, small errors can accumulate quickly along the generated sequence at the inference stage due to the different distributions of training and inference phase.That is because Seq2Seq models minimise single step errors only during training, however the entire sequence has to be generated during the inference phase which generates a discrepancy between training and inference.In this work, we propose a novel curriculum learning based strategy named Temporal Progressive Growing Sampling to effectively bridge the gap between training and inference for spatio-temporal sequence forecasting, by transforming the training process from a fully-supervised manner which utilises all available previous groundtruth values to a less-supervised manner which replaces some of the ground-truth context with generated predictions.To do that we sample the target sequence from midway outputs from intermediate models trained with bigger timescales through a carefully designed decaying strategy.Experimental results demonstrate that our proposed method better models long term dependencies and outperforms baseline approaches on two competitive datasets.

Spatio-temporal trajectory classification is a fundamental problem for location-based services with many real-world applications such as travel mode classification, animal mobility detection, and location recommendation. In the literature, many approaches have been proposed to solve this classification task including deep learning models like LSTM recently for sequence classification. However, these approaches fail to consider both spatial and temporal interval information simultaneously, but share some common drawbacks: omitting either the spatial information or the temporal interval information out. Some models like Time-LSTM, have been proposed to handle the temporal interval information for spatio-temporal trajectories, but they do not take into account the spatial information. Note that, considering both spatial and temporal interval information is crucial for spatio-temporal data mining in order not to miss any spatio-temporal pattern. In this study, we propose a trajectory classifier called Spatio-Temporal GRU to better model the spatio-temporal correlations and irregular temporal intervals prevalently present in spatio-temporal trajectories. We introduce a novel segmented convolutional weight mechanism to capture short-term local spatial correlations in trajectories and propose an additional temporal gate to control the information flow related to the temporal interval information. Performance evaluation demonstrates that our proposed model outperforms popular deep learning approaches for the travel model classification problem.