Structuring personal activity records based on attention-analyzing videos from head mounted camera

Nakamura, Yūichi; Ohde, J.; Ohta, Yuichi

doi:10.1109/icpr.2000.902899

Cited by 10 publications

(7 citation statements)

References 3 publications

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“…IV-C). By departing from traditional frame-based decisions [17], [26], [27], we capture long-term temporal dependencies. As we will see below, doing so is beneficial for detecting subtle periods of engagement and accounting for their variable length.…”

Section: Approachmentioning

confidence: 99%

Detecting Engagement in Egocentric Video

Grauman

2016

Computer Vision – ECCV 2016

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In a wearable camera video, we see what the camera wearer sees. While this makes it easy to know roughly what he chose to look at, it does not immediately reveal when he was engaged with the environment. Specifically, at what moments did his focus linger, as he paused to gather more information about something he saw? Knowing this answer would benefit various applications in video summarization and augmented reality, yet prior work focuses solely on the "what" question (estimating saliency, gaze) without considering the "when" (engagement). We propose a learning-based approach that uses long-term egomotion cues to detect engagement, specifically in browsing scenarios where one frequently takes in new visual information (e.g., shopping, touring). We introduce a large, richly annotated dataset for ego-engagement that is the first of its kind. Our approach outperforms a wide array of existing methods. We show engagement can be detected well independent of both scene appearance and the camera wearer's identity.

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Section: Approachmentioning

confidence: 99%

Detecting Engagement in Egocentric Video

Grauman

2016

Computer Vision – ECCV 2016

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“…When a person is paying attention to visual objects or for these behaviors, head movements are approximately classified into two types of attention events: Active Attention and Passive Attention [10] . "Active Attention" means that we often gaze at something and track it when it attracts our interest.…”

Section: Attention Status Observationmentioning

confidence: 99%

iWearSA: an intelligent wearable surveillance assistant system for perception of moving object

Han

Chen

2007

SPIE Proceedings

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In this paper, we propose iWearSA, an intelligent wearable surveillance assistant system for detecting moving object based on wearable computing technology. The chief objective of iWearSA is to help human complete the surveillance tasks. The research presented here addresses some special challenges and correlative modules in system design, and the block diagram of the iWearSA system is explained in detail. The hardware components used in iWearSA are mentioned. The availability of iWearSA is verified by guarding the kid's activity in public grassplot scenario.

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“…These techniques have been used to understand human cognition and behavior. One example is the use of these techniques in 'life-log' analyses; i.e., capturing personal behavior and experiences using a wearable video/camera system equipped with other sensors and computers on the human body [19,20]. For example, camera and object motions in footage taken from a head-mounted video camera have been analyzed to investigate how humans pay attention to objects [19].…”

Section: Introductionmentioning

confidence: 99%

Application of a computer vision technique to animal-borne video data: extraction of head movement to understand sea turtles’ visual assessment of surroundings

et al. 2015

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Background: An animal-borne video recording system has recently been developed to study the behavior of freeranging animals. In contrast to other types of sensor data (i.e., acceleration), video images offer the advantage of directly acquiring information without analysis. However, most previous findings have only been obtained through visual observation of image data. Here, we demonstrate a new method of data analysis for animal-borne videos using a computer vision technique referred to as template matching. As a case study, we tracked the horizontal head movements of green turtles (Chelonia mydas) to investigate how they move their heads to look around the underwater environment. Results:Template matching allowed tracking of head movements with high accuracy (0.34 ± 0.12 % and 0.52 ± 0.29 % of the root-mean-square error on the x-and y-coordinates, respectively), high true (86.2 ± 8.1 %), and low false extraction rates (6.6 ± 8.4 %). However the program sometimes failed because the turtle's head would move out of range of the video. During cruising swimming, green turtles did not significantly move their heads to one side, moving with a ratio of 50.5:49.5 (left: right). Green turtles moved their heads from side to side more widely and more slowly before (12.0 ± 4.6 point and 0.25 ± 0.03 Hz, respectively) and after taking a breath (27.5 ± 2.9 point and 0.27 ± 0.03 Hz) compared to during cruising swimming (8.4 ± 3.8 point and 0.32 ± 0.01 Hz). Before feeding, turtles moved their heads slowly (0.23 ± 0.03 Hz) and narrowly (9.3 ± 3.6 point). Our combined approach using video and gyro loggers revealed that when making a turn, turtles always turned their heads to the side 1.38 ± 0.77 s before turning their body. Conclusions:Our method enables researchers to quantitatively extract information regarding vision cognition and behavioral responses in green turtles in the wild that could not otherwise be obtained from other sensors used in previous studies. This new method using a combination of computer vision and bio-logging (e.g., gyroscope) can serve as a powerful tool in animal behavior and ecological studies.

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Structuring personal activity records based on attention-analyzing videos from head mounted camera

Cited by 10 publications

References 3 publications

Detecting Engagement in Egocentric Video

Detecting Engagement in Egocentric Video

iWearSA: an intelligent wearable surveillance assistant system for perception of moving object

Application of a computer vision technique to animal-borne video data: extraction of head movement to understand sea turtles’ visual assessment of surroundings

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