Pedestrian Tracking in 360 Video by Virtual PTZ Cameras

Monteleone, Vito; Presti, Liliana Lo; Cascia, Marco La

doi:10.1109/rtsi.2018.8548499

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…Often,

also includes the width

and height

of the bounding box enclosing the target on the image. Many methods have been proposed in literature for tracking by using traditional approaches like linear dynamical systems, Kalman Filter [ 14 , 15 ], re-identification and data association [ 16 , 17 ], and correlation filter [ 18 ] together with hand-crafted features [ 2 , 19 ]. Recently, deep learning has proven to be particularly effective in extracting features for the recognition and detection of objects [ 20 , 21 ], and has been used to solve visual tracking problems [ 2 ].…”

Section: Related Workmentioning

confidence: 99%

Iterative Multiple Bounding-Box Refinements for Visual Tracking

Cruciata¹,

Presti²,

Cascia³

2022

J. Imaging

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Single-object visual tracking aims at locating a target in each video frame by predicting the bounding box of the object. Recent approaches have adopted iterative procedures to gradually refine the bounding box and locate the target in the image. In such approaches, the deep model takes as input the image patch corresponding to the currently estimated target bounding box, and provides as output the probability associated with each of the possible bounding box refinements, generally defined as a discrete set of linear transformations of the bounding box center and size. At each iteration, only one transformation is applied, and supervised training of the model may introduce an inherent ambiguity by giving importance priority to some transformations over the others. This paper proposes a novel formulation of the problem of selecting the bounding box refinement. It introduces the concept of non-conflicting transformations and allows applying multiple refinements to the target bounding box at each iteration without introducing ambiguities during learning of the model parameters. Empirical results demonstrate that the proposed approach improves the iterative single refinement in terms of accuracy and precision of the tracking results.

show abstract

“…Often,

also includes the width

and height

Section: Related Workmentioning

confidence: 99%

Iterative Multiple Bounding-Box Refinements for Visual Tracking

Cruciata¹,

Presti²,

Cascia³

2022

J. Imaging

View full text Add to dashboard Cite

show abstract

“…Works about tracking in 360-degree videos [17] focus mostly on single object tracking by applying deep learning strategies [15,9] and Kalman or particle filters [19,18,3]. The work most similar to ours is the one in [14], which is built upon DeepSORT [22].…”

Section: Related Workmentioning

confidence: 99%

Depth-Aware Multi-object Tracking in Spherical Videos

Presti

Mazzola

Averna

et al. 2022

Image Analysis and Processing – ICIAP 2022

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This paper deals with the multi-object tracking (MOT) problem in videos acquired by 360-degree cameras. Targets are tracked by a frame-by-frame association strategy. At each frame, candidate targets are detected by a pre-trained state-of-the-art deep model. Associations to the targets known till the previous frame are found by solving a data association problem considering the locations of the targets in the scene. In case of a missing detection, a Kalman filter is used to track the target. Differently than works at the state-of-the-art, the proposed tracker considers the depth of the targets in the scene. The distance of the targets from the camera can be estimated by geometrical facts peculiar to the adopted 360-degree camera and by assuming targets move on the groundplane. Distance estimates are used to model the location of the targets in the scene, solve the data association problem, and handle missing detection. Experimental results on publicly available data demonstrate the effectiveness of the adopted approach.

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“…that help to improve the sense of immersivity of the users. From the scientific point of view, because of their features, omnidirectional devices find applications in virtual and augmented reality [1,2], mobile robotics [3,4], and video surveillance [5][6][7], which is also the topic of our work.…”

Section: Introductionmentioning

confidence: 99%

A Dataset of Annotated Omnidirectional Videos for Distancing Applications

et al. 2021

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Omnidirectional (or 360°) cameras are acquisition devices that, in the next few years, could have a big impact on video surveillance applications, research, and industry, as they can record a spherical view of a whole environment from every perspective. This paper presents two new contributions to the research community: the CVIP360 dataset, an annotated dataset of 360° videos for distancing applications, and a new method to estimate the distances of objects in a scene from a single 360° image. The CVIP360 dataset includes 16 videos acquired outdoors and indoors, annotated by adding information about the pedestrians in the scene (bounding boxes) and the distances to the camera of some points in the 3D world by using markers at fixed and known intervals. The proposed distance estimation algorithm is based on geometry facts regarding the acquisition process of the omnidirectional device, and is uncalibrated in practice: the only required parameter is the camera height. The proposed algorithm was tested on the CVIP360 dataset, and empirical results demonstrate that the estimation error is negligible for distancing applications.

show abstract

Pedestrian Tracking in 360 Video by Virtual PTZ Cameras

Cited by 8 publications

References 18 publications

Iterative Multiple Bounding-Box Refinements for Visual Tracking

Iterative Multiple Bounding-Box Refinements for Visual Tracking

Depth-Aware Multi-object Tracking in Spherical Videos

A Dataset of Annotated Omnidirectional Videos for Distancing Applications

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