Integrating Egocentric Videos in Top-View Surveillance Videos: Joint Identification and Temporal Alignment

Ardeshir, Shervin; Borji, Ali

doi:10.1007/978-3-030-01252-6_18

Cited by 17 publications

(16 citation statements)

References 31 publications

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“…Ardeshir and Borji (2016;2018a) propose a method to identify camera wearers on a top-view video given the horizontalview videos recorded by these wearable cameras. Similarly, given a horizontal video and a top-view video, Ardeshir and Borji (2018b) study how to identify the horizontalview camera holder in the top-view video, and re-identify the subjects present in both the horizontal-and top-view videos. Han et al (2019) exploit the spatial distribution of the subjects to match all the subjects between the top-and horizontal-views, and builds the subject association across the two views.…”

Section: Related Workmentioning

confidence: 99%

Complementary-View Multiple Human Tracking

Han

Feng

Zhao

et al. 2020

AAAI

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The global trajectories of targets on ground can be well captured from a top view in a high altitude, e.g., by a drone-mounted camera, while their local detailed appearances can be better recorded from horizontal views, e.g., by a helmet camera worn by a person. This paper studies a new problem of multiple human tracking from a pair of top- and horizontal-view videos taken at the same time. Our goal is to track the humans in both views and identify the same person across the two complementary views frame by frame, which is very challenging due to very large field of view difference. In this paper, we model the data similarity in each view using appearance and motion reasoning and across views using appearance and spatial reasoning. Combing them, we formulate the proposed multiple human tracking as a joint optimization problem, which can be solved by constrained integer programming. We collect a new dataset consisting of top- and horizontal-view video pairs for performance evaluation and the experimental results show the effectiveness of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Complementary-View Multiple Human Tracking

Han

Feng

Zhao

et al. 2020

AAAI

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“…Some surveys, especially those that focus on fixed multi-view videos, can be founded in [12,30]. Recently, person identification via wearable cameras has been advanced [1,9,29]. These approaches can use appearance cues (shape, color, motion) for person identification.…”

Section: Person Identification From Multiple Videosmentioning

confidence: 99%

Gravity-Direction-Aware Joint Inter-Device Matching and Temporal Alignment between Camera and Wearable Sensors

Ishihara

Kumano²

2020

Companion Publication of the 2020 International Conference on Multimodal Interaction

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“…Prior works [1,2,3] tried to alleviate the challenge of this problem by assuming 1) the view direction of the top-view camera in the air has certain slope such that subjects' body, and even part of the background, are still partially visible in top views and can be used for feature matching to the horizontal views, and 2) the view angle of the horizontalview camera on the ground is consistent with the moving direction of the camera wearer and can be easily estimated by computing optical flow in the top-view videos This can be used to identify the on-the-ground camera wearer in the top-view video. These two assumptions, however, limit the their applicability in practice, e.g., the horizontal-view camera wearer may turn head (and therefore the head-mounted camera) when he walks, leading to inconsistency between his moving direction and wearable-camera view direction.…”

Section: Top-viewmentioning

confidence: 99%

Multiple Human Association between Top and Horizontal Views by Matching Subjects' Spatial Distributions

Han,

Zhang,

Feng

et al. 2019

Preprint

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Video surveillance can be significantly enhanced by using both top-view data, e.g., those from drone-mounted cameras in the air, and horizontal-view data, e.g., those from wearable cameras on the ground. Collaborative analysis of different-view data can facilitate various kinds of applications, such as human tracking, person identification, and human activity recognition. However, for such collaborative analysis, the first step is to associate people, referred to as subjects in this paper, across these two views. This is a very challenging problem due to large human-appearance difference between top and horizontal views. In this paper, we present a new approach to address this problem by exploring and matching the subjects' spatial distributions between the two views. More specifically, on the top-view image, we model and match subjects' relative positions to the horizontal-view camera in both views and define a matching cost to decide the actual location of horizontal-view camera and its view angle in the top-view image. We collect a new dataset consisting of top-view and horizontal-view image pairs for performance evaluation and the experimental results show the effectiveness of the proposed method.

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Integrating Egocentric Videos in Top-View Surveillance Videos: Joint Identification and Temporal Alignment

Cited by 17 publications

References 31 publications

Complementary-View Multiple Human Tracking

Complementary-View Multiple Human Tracking

Gravity-Direction-Aware Joint Inter-Device Matching and Temporal Alignment between Camera and Wearable Sensors

Multiple Human Association between Top and Horizontal Views by Matching Subjects' Spatial Distributions

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