Real-Time Person Detection and Tracking in Panoramic Video

Thaler, Marcus; Bailer, Werner

doi:10.1109/cvprw.2013.149

Cited by 9 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite over the years there was a huge interest in people detection and tracking, only a few works focused on the possibility of exploiting panoramic videos. In [22], authors propose a people tracking system on panoramic videos obtained from a system of multiple cameras. They focused on real-time processing, relying on a HOG detector and an optical flow detector tracker.…”

Section: People Trackingmentioning

confidence: 99%

People Tracking in Panoramic Video for Guiding Robots

Bacchin¹,

Berno²,

Menegatti³

et al. 2022

Preprint

View full text Add to dashboard Cite

A guiding robot aims to effectively bring people to and from specific places within environments that are possibly unknown to them. During this operation the robot should be able to detect and track the accompanied person, trying never to lose sight of her/him. A solution to minimize this event is to use an omnidirectional camera: its 360 • Field of View (FoV) guarantees that any framed object cannot leave the FoV if not occluded or very far from the sensor. However, the acquired panoramic videos introduce new challenges in perception tasks such as people detection and tracking, including the large size of the images to be processed, the distortion effects introduced by the cylindrical projection and the periodic nature of panoramic images. In this paper, we propose a set of targeted methods that allow to effectively adapt to panoramic videos a standard people detection and tracking pipeline originally designed for perspective cameras. Our methods have been implemented and tested inside a deep learning-based people detection and tracking framework with a commercial 360 • camera. Experiments performed on datasets specifically acquired for guiding robot applications and on a real service robot show the effectiveness of the proposed approach over other state-of-the-art systems. We release with this paper the acquired and annotated datasets and the open-source implementation of our method.

show abstract

Section: People Trackingmentioning

confidence: 99%

People Tracking in Panoramic Video for Guiding Robots

Bacchin¹,

Berno²,

Menegatti³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The task poses additional challenges to generic object detection, including camera distortion and fast movement (for broadcast videos), as well as frequent crowded scenes. Thaler et al [17] and Faulkner et al [7] adopt HOG and Haar features to detect and classify players in a sliding window for soccer and football. Acuna [1] introduces an end-to-end CNN-based object detector in the basketball.…”

Section: Related Work 21 Object Detection In Sportsmentioning

confidence: 99%

Detecting and Matching Related Objects with One Proposal Multiple Predictions

Liu¹,

Hafemann²,

Jamieson³

et al. 2021

Preprint

View full text Add to dashboard Cite

Tracking players in sports videos is commonly done in a tracking-by-detection framework, first detecting players in each frame, and then performing association over time. While for some sports tracking players is sufficient for game analysis, sports like hockey, tennis and polo may require additional detections, that include the object the player is holding (e.g. racket, stick). The baseline solution for this problem involves detecting these objects as separate classes, and matching them to player detections based on the intersection over union (IoU). This approach, however, leads to poor matching performance in crowded situations, as it does not model the relationship between players and objects. In this paper, we propose a simple yet efficient way to detect and match players and related objects at once without extra cost, by considering an implicit association for prediction of multiple objects through the same proposal box. We evaluate the method on a dataset of broadcast ice hockey videos, and also a new public dataset we introduce called COCO +Torso. On the ice hockey dataset, the proposed method boosts matching performance from 57.1% to 81.4%, while also improving the meanAP of player+stick detections from 68.4% to 88.3%. On the COCO +Torso dataset, we see matching improving from 47.9% to 65.2%. The COCO +Torso dataset, code and pre-trained models will be released at https: //github.com/foreverYoungGitHub/detectand-match-related-objects.

show abstract

“…Panoramic cameras are rapidly gaining in popularity, resulting in the researchers' focus on image processing in 360 • videos, e.g., Hu and Lin et al [10] who designed an agent to control the viewing angle in panoramic sports videos. In 2013, Marcus and Werner proposed a methodology for real-time person tracking in panoramic videos [11]. Some other human detection and tracking methods are also in the literature [12][13][14], which show that object tracking is currently a hot research topic.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Application for Generating Multiple Experiences from 360° Panoramic Video by Tracking Arbitrary Objects and Viewer’s Orientations

Shah¹,

Han²,

Lee³

2020

Applied Sciences

View full text Add to dashboard Cite

We propose a novel authoring and viewing system for generating multiple experiences with a single 360° video and efficiently transferring these experiences to the user. An immersive video contains much more interesting information within the 360° environment than normal videos. There can be multiple interesting areas within a 360° frame at the same time. Due to the narrow field of view in virtual reality head-mounted displays, a user can only view a limited area of a 360° video. Hence, our system is aimed at generating multiple experiences based on interesting information in different regions of a 360° video and efficient transferring of these experiences to prospective users. The proposed system generates experiences by using two approaches: (1) Recording of the user’s experience when the user watches a panoramic video using a virtual reality head-mounted display, and (2) tracking of an arbitrary interesting object in a 360° video selected by the user. For tracking of an arbitrary interesting object, we have developed a pipeline around an existing simple object tracker to adapt it for 360° videos. This tracking algorithm was performed in real time on a CPU with high precision. Moreover, to the best of our knowledge, there is no such existing system that can generate a variety of different experiences from a single 360° video and enable the viewer to watch one 360° visual content from various interesting perspectives in immersive virtual reality. Furthermore, we have provided an adaptive focus assistance technique for efficient transferring of the generated experiences to other users in virtual reality. In this study, technical evaluation of the system along with a detailed user study has been performed to assess the system’s application. Findings from evaluation of the system showed that a single 360° multimedia content has the capability of generating multiple experiences and transfers among users. Moreover, sharing of the 360° experiences enabled viewers to watch multiple interesting contents with less effort.

show abstract

Real-Time Person Detection and Tracking in Panoramic Video

Cited by 9 publications

References 8 publications

People Tracking in Panoramic Video for Guiding Robots

People Tracking in Panoramic Video for Guiding Robots

Detecting and Matching Related Objects with One Proposal Multiple Predictions

Real-Time Application for Generating Multiple Experiences from 360° Panoramic Video by Tracking Arbitrary Objects and Viewer’s Orientations

Contact Info

Product

Resources

About