TAU: A Framework for Video-Based Traffic Analytics Leveraging Artificial Intelligence and Unmanned Aerial Systems

• URL: http://arxiv.org/abs/2303.00337v1
• Date: Wed, 1 Mar 2023 09:03:44 GMT
• Title: TAU: A Framework for Video-Based Traffic Analytics Leveraging Artificial Intelligence and Unmanned Aerial Systems
• Authors: Bilel Benjdira, Anis Koubaa, Ahmad Taher Azar, Zahid Khan, Adel Ammar, Wadii Boulila
• Abstract summary: We develop an AI-integrated video analytics framework, called TAU (Traffic Analysis from UAVs), for automated traffic analytics and understanding. Unlike previous works on traffic video analytics, we propose an automated object detection and tracking pipeline from video processing to advanced traffic understanding using high-resolution UAV images.
• Score: 2.748428882236308
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Smart traffic engineering and intelligent transportation services are in increasing demand from governmental authorities to optimize traffic performance and thus reduce energy costs, increase the drivers' safety and comfort, ensure traffic laws enforcement, and detect traffic violations. In this paper, we address this challenge, and we leverage the use of Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) to develop an AI-integrated video analytics framework, called TAU (Traffic Analysis from UAVs), for automated traffic analytics and understanding. Unlike previous works on traffic video analytics, we propose an automated object detection and tracking pipeline from video processing to advanced traffic understanding using high-resolution UAV images. TAU combines six main contributions. First, it proposes a pre-processing algorithm to adapt the high-resolution UAV image as input to the object detector without lowering the resolution. This ensures an excellent detection accuracy from high-quality features, particularly the small size of detected objects from UAV images. Second, it introduces an algorithm for recalibrating the vehicle coordinates to ensure that vehicles are uniquely identified and tracked across the multiple crops of the same frame. Third, it presents a speed calculation algorithm based on accumulating information from successive frames. Fourth, TAU counts the number of vehicles per traffic zone based on the Ray Tracing algorithm. Fifth, TAU has a fully independent algorithm for crossroad arbitration based on the data gathered from the different zones surrounding it. Sixth, TAU introduces a set of algorithms for extracting twenty-four types of insights from the raw data collected. The code is shared here: https://github.com/bilel-bj/TAU. Video demonstrations are provided here: https://youtu.be/wXJV0H7LviU and here: https://youtu.be/kGv0gmtVEbI.

Related papers

• Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery [4.387337528923525]
  This paper presents a framework for extracting georeferenced vehicle trajectories from high-altitude drone footage. We employ state-of-the-art computer vision and deep learning to create an end-to-end pipeline. Results demonstrate the potential of integrating drone technology with advanced computer vision for precise, cost-effective urban traffic monitoring.
  arXiv  Detail & Related papers  (2024-11-04T14:49:01Z)
• MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial and temporal structures in vehicle traffic [2.8068840920981484]
  This paper aims to model vehicle tracking using computer vision to detect traffic anomalies on a highway. We develop the steps of detection, tracking, and analysis of traffic. Experimental results show that our method is acceptable on the Track4 test set.
  arXiv  Detail & Related papers  (2023-10-28T00:36:50Z)
• Traffic-Domain Video Question Answering with Automatic Captioning [69.98381847388553]
  Video Question Answering (VidQA) exhibits remarkable potential in facilitating advanced machine reasoning capabilities. We present a novel approach termed Traffic-domain Video Question Answering with Automatic Captioning (TRIVIA), which serves as a weak-supervision technique for infusing traffic-domain knowledge into large video-language models.
  arXiv  Detail & Related papers  (2023-07-18T20:56:41Z)
• SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving [160.57870373052577]
  We propose a saliency-based OD algorithm (SalienDet) to detect unknown objects. Our SalienDet utilizes a saliency-based algorithm to enhance image features for object proposal generation. We design a dataset relabeling approach to differentiate the unknown objects from all objects in training sample set to achieve Open-World Detection.
  arXiv  Detail & Related papers  (2023-05-11T16:19:44Z)
• AZTR: Aerial Video Action Recognition with Auto Zoom and Temporal Reasoning [63.628195002143734]
  We propose a novel approach for aerial video action recognition. Our method is designed for videos captured using UAVs and can run on edge or mobile devices. We present a learning-based approach that uses customized auto zoom to automatically identify the human target and scale it appropriately.
  arXiv  Detail & Related papers  (2023-03-02T21:24:19Z)
• Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing [77.34726150561087]
  This paper describes how advanced deep learning based computer vision algorithms are applied to enable real-time on-board sensor processing for small UAVs. All algorithms have been developed using state-of-the-art image processing methods based on deep neural networks.
  arXiv  Detail & Related papers  (2022-11-02T11:10:42Z)
• Traffic-Net: 3D Traffic Monitoring Using a Single Camera [1.1602089225841632]
  We provide a practical platform for real-time traffic monitoring using a single CCTV traffic camera. We adapt a custom YOLOv5 deep neural network model for vehicle/pedestrian detection and an enhanced SORT tracking algorithm. We also develop a hierarchical traffic modelling solution based on short- and long-term temporal video data stream.
  arXiv  Detail & Related papers  (2021-09-19T16:59:01Z)
• FOVEA: Foveated Image Magnification for Autonomous Navigation [53.69803081925454]
  We propose an attentional approach that elastically magnifies certain regions while maintaining a small input canvas. Our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning. On the autonomous driving datasets Argoverse-HD and BDD100K, we show our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning.
  arXiv  Detail & Related papers  (2021-08-27T03:07:55Z)
• CFTrack: Center-based Radar and Camera Fusion for 3D Multi-Object Tracking [9.62721286522053]
  We propose an end-to-end network for joint object detection and tracking based on radar and camera sensor fusion. Our proposed method uses a center-based radar-camera fusion algorithm for object detection and utilizes a greedy algorithm for object association. We evaluate our method on the challenging nuScenes dataset, where it achieves 20.0 AMOTA and outperforms all vision-based 3D tracking methods in the benchmark.
  arXiv  Detail & Related papers  (2021-07-11T23:56:53Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)
• Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis [3.093890460224435]
  Authors deploy several state of the art object detection and tracking algorithms to detect and track different classes of vehicles. Model combinations are validated and compared against the manually counted ground truths of over 9 hours' traffic video data. Results demonstrate that the combination of CenterNet and Deep SORT, Detectron2 and Deep SORT, and YOLOv4 and Deep SORT produced the best overall counting percentage for all vehicles.
  arXiv  Detail & Related papers  (2020-07-31T17:49:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.