A novel efficient Multi-view traffic-related object detection framework

• URL: http://arxiv.org/abs/2302.11810v1
• Date: Thu, 23 Feb 2023 06:42:37 GMT
• Title: A novel efficient Multi-view traffic-related object detection framework
• Authors: Kun Yang, Jing Liu, Dingkang Yang, Hanqi Wang, Peng Sun, Yanni Zhang, Yan Liu, Liang Song
• Abstract summary: We propose a novel traffic-related framework named CEVAS to achieve efficient object detection using multi-view video data. Results show that our framework significantly reduces response latency while achieving the same detection accuracy as the state-of-the-art methods.
• Score: 17.50049841016045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of intelligent transportation system applications, a tremendous amount of multi-view video data has emerged to enhance vehicle perception. However, performing video analytics efficiently by exploiting the spatial-temporal redundancy from video data remains challenging. Accordingly, we propose a novel traffic-related framework named CEVAS to achieve efficient object detection using multi-view video data. Briefly, a fine-grained input filtering policy is introduced to produce a reasonable region of interest from the captured images. Also, we design a sharing object manager to manage the information of objects with spatial redundancy and share their results with other vehicles. We further derive a content-aware model selection policy to select detection methods adaptively. Experimental results show that our framework significantly reduces response latency while achieving the same detection accuracy as the state-of-the-art methods.

Related papers

• An object detection approach for lane change and overtake detection from motion profiles [3.545178658731506]
  In this paper, we address the identification of overtake and lane change maneuvers with a novel object detection approach applied to motion profiles. To train and test our model we created an internal dataset of motion profile images obtained from a heterogeneous set of dashcam videos. In addition to a standard object-detection approach, we show how the inclusion of CoordConvolution layers further improves the model performance.
  arXiv  Detail & Related papers  (2025-02-06T17:36:35Z)
• When language and vision meet road safety: leveraging multimodal large language models for video-based traffic accident analysis [6.213279061986497]
  SeeUnsafe is a framework that transforms video-based traffic accident analysis into a more interactive, conversational approach. Our framework employs a multimodal-based aggregation strategy to handle videos of various lengths and generate structured responses for review and evaluation. We conduct extensive experiments on the Toyota Woven Traffic Safety dataset, demonstrating that SeeUnsafe effectively performs accident-aware video classification and visual grounding.
  arXiv  Detail & Related papers  (2025-01-17T23:35:34Z)
• Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation [49.113131249753714]
  We propose an efficient algorithm, termed MTNet, which concurrently exploits motion and temporal cues. MTNet is devised by effectively merging appearance and motion features during the feature extraction process within encoders. We employ a cascade of decoders all feature levels across all feature levels to optimally exploit the derived features.
  arXiv  Detail & Related papers  (2025-01-14T03:15:46Z)
• Video traffic identification with novel feature extraction and selection method [1.7709344190822938]
  This study proposes to extract video-related features to construct a large-scale feature set to identify video traffic. Second, to reduce the cost of video traffic identification and select an effective feature subset, the current research proposes an adaptive distribution distance-based feature selection (ADDFS) method. Experimental results suggest that the proposed method can achieve high identification performance for video scene traffic and cloud game video traffic identification.
  arXiv  Detail & Related papers  (2023-03-06T14:26:24Z)
• Real-Time Accident Detection in Traffic Surveillance Using Deep Learning [0.8808993671472349]
  This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. The proposed framework consists of three hierarchical steps, including efficient and accurate object detection based on the state-of-the-art YOLOv4 method. The robustness of the proposed framework is evaluated using video sequences collected from YouTube with diverse illumination conditions.
  arXiv  Detail & Related papers  (2022-08-12T19:07:20Z)
• Implicit Motion Handling for Video Camouflaged Object Detection [60.98467179649398]
  We propose a new video camouflaged object detection (VCOD) framework. It can exploit both short-term and long-term temporal consistency to detect camouflaged objects from video frames.
  arXiv  Detail & Related papers  (2022-03-14T17:55:41Z)
• Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction [110.61383502442598]
  We introduce a novel neural network framework termed Cross-Modal Message Propagation Network (CMMPNet) CMMPNet is composed of two deep Auto-Encoders for modality-specific representation learning and a tailor-designed Dual Enhancement Module for cross-modal representation refinement. Experiments on three real-world benchmarks demonstrate the effectiveness of our CMMPNet for robust road extraction.
  arXiv  Detail & Related papers  (2021-11-30T04:30:10Z)
• Video Salient Object Detection via Contrastive Features and Attention Modules [106.33219760012048]
  We propose a network with attention modules to learn contrastive features for video salient object detection. A co-attention formulation is utilized to combine the low-level and high-level features. We show that the proposed method requires less computation, and performs favorably against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-11-03T17:40:32Z)
• Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach [19.59528430884104]
  We present a neural network model called the Recurrent Correlational Network, where detection and tracking are jointly performed. In experiments with datasets containing images of scenes with small flying objects, such as birds and unmanned aerial vehicles, the proposed method yielded consistent improvements. Our network performs as well as state-of-the-art generic object trackers when it was evaluated as a tracker on a bird image dataset.
  arXiv  Detail & Related papers  (2021-05-18T03:22:03Z)
• Ensembling object detectors for image and video data analysis [98.26061123111647]
  We propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data. We extend it to video data by proposing a two-stage tracking-based scheme for detection refinement.
  arXiv  Detail & Related papers  (2021-02-09T12:38:16Z)
• SoDA: Multi-Object Tracking with Soft Data Association [75.39833486073597]
  Multi-object tracking (MOT) is a prerequisite for a safe deployment of self-driving cars. We propose a novel approach to MOT that uses attention to compute track embeddings that encode dependencies between observed objects.
  arXiv  Detail & Related papers  (2020-08-18T03:40:25Z)

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.