The AI City Challenge was created to accelerate intelligent video analysis that helps make cities smarter and safer. Transportation is one of the largest segments that can benefit from actionable insights derived from data captured by sensors, where computer vision and deep learning have shown promise in achieving large-scale practical deployment. The 4th annual edition of the AI City Challenge has attracted 315 participating teams across 37 countries, who leverage city-scale real traffic data and high-quality synthetic data to compete in four challenge tracks. Track 1 addressed video-based automatic vehicle counting, where the evaluation is conducted on both algorithmic effectiveness and computational efficiency. Track 2 addressed city-scale vehicle re-identification with augmented synthetic data to substantially increase the training set for the task. Track 3 addressed city-scale multi-target multi-camera vehicle tracking. Track 4 addressed traffic anomaly detection. The evaluation system shows two leader boards, in which a general leader board shows all submitted results, and a public leader board shows results limited to our contest participation rules, that teams are not allowed to use external data in their work. The general leader board shows results more close to real-world situations where annotated data are limited. Our results show promise that AI technology can enable smarter and safer transportation systems.

/pdf/the-4th-ai-city-challenge-5f5gaxo728.pdf

The 4th AI City Challenge

Multi-camera tracking of vehicles on a city-scale level is a crucial task for efficient traffic monitoring. Most of the errors made by such multi-target multi-camera tracking systems arise due to tracking failures or misleading visual information of detection boxes under occlusion. Therefore, we propose an occlusion-aware approach that leverages temporal information from tracks to improve the single-camera tracking performance by an occlusion handling strategy and additional modules to filter false detections. For the multi-camera tracking, we discard obstacle-occluded detection boxes by a background filtering technique and boxes overlapping with other targets using the available track information to improve the quality of extracted visual features. Furthermore, topological and temporal constraints are incorporated to simplify the re-identification task in the multi-camera clustering. We give detailed insights into our method with ablative experiments and show its competitiveness on the CityFlowV2 dataset, where we achieve promising results ranking 4th in Track 3 of the 2021 AI City Challenge.

/pdf/an-occlusion-aware-multi-target-multi-camera-tracking-system-3mmmpn6ty5.pdf

An Occlusion-aware Multi-target Multi-camera Tracking System

Traffic surveillance cameras are the eyes of the Intelligent Transportation Systems (ITS). However, they are currently isolated and can only extract information from each of their fixed views. To track vehicles across multiple cameras and help public agencies collect link travel time and speed information, an Edge-empowered Cooperative Multi-camera Sensing (ECoMS) System is proposed. ECoMS system presents a novel algorithmic and edge-server cooperative system construct to push edge computing and multi-camera re-identification workflow serving for traffic sensing based on Internet of Things (IoT) architecture. On the algorithm side, ECoMS system proposes a featherlight edge-based computer vision framework for vehicle detection, tracking, and features selection process in a real-time manner. Then, by only sending the objects’ representations to the server, the high-bandwidth data transmission and the heavy post-processing system can be abandoned. Furthermore, a hierarchical clip-based deep vehicle re-identification framework is proposed and integrated into the ECoMS system, and significantly outperforms other state-of-the-art methods by 4%–8% on Rank-1 accuracy. Finally, to balance the accuracy level of different camera pairs, a collaborative cross-camera traffic information estimation framework based on kernel density estimation with kernel smoother is implemented, which can get the precise link and region traffic information together with distributions (less than 1.01 KL distance). By maximizing the cooperation of the computational resources, orchestrating the data transmission and integrating road network graph features in the system, the ECoMS can precisely model the network-scale traffic information in a flexible, cost-effective, and scalable workflow. To the author’s best knowledge, ECoMS is the first multi-camera vehicle tracking and traffic monitoring system based on cooperative IoT architecture. 

Cooperative multi-camera vehicle tracking and traffic surveillance with edge artificial intelligence and representation learning

Multi-camera tracking of vehicles on a city-wide level is a core component of modern traffic monitoring systems. For this task, single-camera tracking failures are the most common causes of errors concerning automatic multi-target multi-camera tracking systems. To address these problems, we propose several modules that aim at improving single-camera tracklets, e.g., appearance-based tracklet splitting, single-camera clustering, and track completion. After these track refinement steps, hierarchical clustering is used to associate the enhanced single-camera tracklets. During this stage, we leverage vehicle re-identification features as well as prior knowledge about the scene's topology. Last, the proposed track completion strategy is adopted for the cross-camera association task to obtain the final multi-camera tracks. Our method proves itself competitive: With it, we achieved 4th place in track 1 of the 2022 AI City Challenge. 

Improving Multi-Target Multi-Camera Tracking by Track Refinement and Completion

Traffic event retrieval is one of the important tasks for intelligent traffic system management. To find accurate candidate events in traffic videos corresponding to a specific text query, it is necessary to understand the text query’s attributes, represent the visual and motion attributes of vehicles in videos, and measure the similarity between them. Thus we propose a promising method for vehicle event retrieval from a natural-language-based specification. We utilize both appearance and motion attributes of a vehicle and adapt the COOT model to evaluate the semantic relationship between a query and a video track. Experiments with the test dataset of Track 5 in AI City Challenge 2021 show that our method is among the top 6 with a score of 0.1560.

/pdf/traffic-video-event-retrieval-via-text-query-using-vehicle-97508vp10m.pdf

Traffic Video Event Retrieval via Text Query using Vehicle Appearance and Motion Attributes

Automated re-identification (re-id) of vehicles is the foundation of many traffic analysis applications across camera networks, e.g. vehicle tracking, counting, or traffic density and flow estimation. The re-id task is made difficult by variations in lighting, viewpoint, image quality and similar vehicle models and colors that can occur across the network. These influences can cause a high visual appearance variation for the same vehicle while different vehicles may appear near identical under similar conditions. However, with a growing number of available datasets and well crafted deep learning models, much progress has been made. In this work we summarize the results of our participation in the NVIDIA AI City Challenge 2020 for vehicle reid. We address the re-id task by relying on well-proven design choices from the closely related person re-id literature. In addition to this, we explicitly address viewpoint and occlusions variation. The former is addressed by incorporating vehicle viewpoint classification results into our matching distance. The required viewpoint classifier is trained predominantly on simulated data and we show that it can be applied to real-world imagery with minimal domain adaptation. We address occlusion by relying on temporal attention scores which emphasize video frames that contain minimal occlusion. Finally, we further boost re-id accuracy by applying video-based re-ranking and an ensemble of complementary models. Our models, code, and simulated data is available at https://github.com/corner100/2020-aicitychallenge-IOSB-VeRi.

/pdf/large-scale-vehicle-re-identification-by-knowledge-transfer-3pq1pwyfde.pdf

Large Scale Vehicle Re-Identification by Knowledge Transfer From Simulated Data and Temporal Attention

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Large Scale Vehicle Re-Identification by Knowledge Transfer From Simulated Data and Temporal Attention