Tracking Passengers and Baggage Items using Multi-camera Systems at Security Checkpoints

• URL: http://arxiv.org/abs/2007.07924v2
• Date: Tue, 22 Feb 2022 17:48:37 GMT
• Title: Tracking Passengers and Baggage Items using Multi-camera Systems at Security Checkpoints
• Authors: Abubakar Siddique and Henry Medeiros
• Abstract summary: We introduce a novel tracking-by-detection framework to track multiple objects in overhead camera videos for airport checkpoint security scenarios. Our approach improves object detection by employing a test-time data augmentation procedure. An evaluation of detection, tracking, and association performances on videos obtained from multiple overhead cameras in a realistic airport checkpoint environment demonstrates the effectiveness of the proposed approach.
• Score: 0.7424262881242935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a novel tracking-by-detection framework to track multiple objects in overhead camera videos for airport checkpoint security scenarios where targets correspond to passengers and their baggage items. Our approach improves object detection by employing a test-time data augmentation procedure that provides multiple geometrically transformed images as inputs to a convolutional neural network. We cluster the multiple detections generated by the network using the mean-shift algorithm. The multiple hypothesis tracking algorithm then keeps track of the temporal identifiers of the targets based on the cluster centroids. Our method also incorporates a trajectory association mechanism to maintain the consistency of the temporal identifiers as passengers travel across camera views. Finally, we also introduce a simple distance-based matching mechanism to associate passengers with their luggage. An evaluation of detection, tracking, and association performances on videos obtained from multiple overhead cameras in a realistic airport checkpoint environment demonstrates the effectiveness of the proposed approach.

Related papers

• MCTR: Multi Camera Tracking Transformer [45.66952089591361]
  Multi-Camera Tracking tRansformer (MCTR) is a novel end-to-end approach tailored for multi-object detection and tracking across multiple cameras. MCTR leverages end-to-end detectors like DEtector TRansformer (DETR) to produce detections and detection embeddings independently for each camera view. The framework maintains set of track embeddings that encaplusate global information about the tracked objects, and updates them at every frame by integrating local information from the view-specific detection embeddings.
  arXiv  Detail & Related papers  (2024-08-23T17:37:03Z)
• SeMoLi: What Moves Together Belongs Together [51.72754014130369]
  We tackle semi-supervised object detection based on motion cues. Recent results suggest that motion-based clustering methods can be used to pseudo-label instances of moving objects. We re-think this approach and suggest that both, object detection, as well as motion-inspired pseudo-labeling, can be tackled in a data-driven manner.
  arXiv  Detail & Related papers  (2024-02-29T18:54:53Z)
• Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment [22.531044994763487]
  We propose a novel multi-camera multiple people tracking method that uses anchor clustering-guided for cross-camera reassigning. Our approach aims to improve accuracy of tracking by identifying key features that are unique to every individual. The method has demonstrated robustness and effectiveness in handling both synthetic and real-world data.
  arXiv  Detail & Related papers  (2023-04-19T07:38:15Z)
• DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes [74.64897845999677]
  We introduce a new cross-view multi-object tracking dataset for DIVerse Open scenes with dense tracking pedestrians. Our DIVOTrack has fifteen distinct scenarios and 953 cross-view tracks, surpassing all cross-view multi-object tracking datasets currently available. Furthermore, we provide a novel baseline cross-view tracking method with a unified joint detection and cross-view tracking framework named CrossMOT.
  arXiv  Detail & Related papers  (2023-02-15T14:10:42Z)
• Tracking Passengers and Baggage Items using Multiple Overhead Cameras at Security Checkpoints [2.021502591596062]
  We introduce a novel framework to track multiple objects in overhead camera videos for airport checkpoint security scenarios. We propose a Self-Supervised Learning (SSL) technique to provide the model information about instance segmentation uncertainty from overhead images. Our results show that self-supervision improves object detection accuracy by up to $42%$ without increasing the inference time of the model.
  arXiv  Detail & Related papers  (2022-12-31T12:57:09Z)
• Tracking by Associating Clips [110.08925274049409]
  In this paper, we investigate an alternative by treating object association as clip-wise matching. Our new perspective views a single long video sequence as multiple short clips, and then the tracking is performed both within and between the clips. The benefits of this new approach are two folds. First, our method is robust to tracking error accumulation or propagation, as the video chunking allows bypassing the interrupted frames. Second, the multiple frame information is aggregated during the clip-wise matching, resulting in a more accurate long-range track association than the current frame-wise matching.
  arXiv  Detail & Related papers  (2022-12-20T10:33:17Z)
• 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)
• Cross-Camera Trajectories Help Person Retrieval in a Camera Network [124.65912458467643]
  Existing methods often rely on purely visual matching or consider temporal constraints but ignore the spatial information of the camera network. We propose a pedestrian retrieval framework based on cross-camera generation, which integrates both temporal and spatial information. To verify the effectiveness of our method, we construct the first cross-camera pedestrian trajectory dataset.
  arXiv  Detail & Related papers  (2022-04-27T13:10:48Z)
• Online Clustering-based Multi-Camera Vehicle Tracking in Scenarios with overlapping FOVs [2.6365690297272617]
  Multi-Target Multi-Camera (MTMC) vehicle tracking is an essential task of visual traffic monitoring. We present a new low-latency online approach for MTMC tracking in scenarios with partially overlapping fields of view.
  arXiv  Detail & Related papers  (2021-02-08T09:55:55Z)
• TrackFormer: Multi-Object Tracking with Transformers [92.25832593088421]
  TrackFormer is an end-to-end multi-object tracking and segmentation model based on an encoder-decoder Transformer architecture. New track queries are spawned by the DETR object detector and embed the position of their corresponding object over time. TrackFormer achieves a seamless data association between frames in a new tracking-by-attention paradigm.
  arXiv  Detail & Related papers  (2021-01-07T18:59:29Z)
• Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets [96.98888948518815]
  State-of-the-art multi-object tracking(MOT) methods follow the tracking-by-detection paradigm. We propose a new MOT paradigm, tracking-by-counting, tailored for crowded scenes.
  arXiv  Detail & Related papers  (2020-07-18T19:51:53Z)

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.