Taking the bite out of automated naming of characters in TV video

Everingham, Mark; Šivic, Josef; Zisserman, Andrew

doi:10.1016/j.imavis.2008.04.018

Cited by 164 publications

(181 citation statements)

References 35 publications

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“…For point tracking we use the KLT tracker [24] which uses optical flow to track sparse interest points for L frames, where L is a parameter. To determine whether two subsequent detection bounding boxes A and B belong to the same unique animal we use the intersection over union measure A∩B A∪B > 0.5 of the set of point tracks through A and through B [7].…”

Section: Animal Countingmentioning

confidence: 99%

Nature Conservation Drones for Automatic Localization and Counting of Animals

Gemert

Verschoor²,

Mettes

et al. 2015

Lecture Notes in Computer Science

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General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Abstract. This paper is concerned with nature conservation by automatically monitoring animal distribution and animal abundance. Typically, such conservation tasks are performed manually on foot or after an aerial recording from a manned aircraft. Such manual approaches are expensive, slow and labor intensive. In this paper, we investigate the combination of small unmanned aerial vehicles (UAVs or "drones") with automatic object recognition techniques as a viable solution to manual animal surveying. Since no controlled data is available, we record our own animal conservation dataset with a quadcopter drone. We evaluate two nature conservation tasks: i) animal detection ii) animal counting using three state-of-the-art generic object recognition methods that are particularly well-suited for on-board detection. Results show that object detection techniques for human-scale photographs do not directly translate to a drone perspective, but that light-weight automatic object detection techniques are promising for nature conservation tasks.

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Section: Animal Countingmentioning

confidence: 99%

Nature Conservation Drones for Automatic Localization and Counting of Animals

Gemert

Verschoor²,

Mettes

et al. 2015

Lecture Notes in Computer Science

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“…In this section, an approach to face recognition based on facial feature localization from [21] is explained. This approach rst detects facial features and than extracts local appearance based descriptors at where the features were found.…”

Section: Face Recognitionmentioning

confidence: 99%

Monitoring Interactions

Meißner¹,

Veltkamp²

2016

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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This work proposes a human interaction recognition based approach to video indexing that represents a video by showing when and with whom was interacted throughout the video. In order to visualize the length of an interaction, it is required to recognize individuals that have been detected in earlier parts of the video. To solve this problem, an approach to photo-clustering is extended to video material by tracking detected faces and using the information from tracking to improve the recognition of human beings. The results of the tracking based approach show a considerable decrease of false cluster assignments compared to the original method. Further, it is demonstrated that the proposed method is able to correctly recognize the appearance of ve out of the six individuals correctly.iii iv

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“…Everingham et al [26,27] addressed the problem of automatically labeling faces of characters in TV or film materials with their names. Similar to the "Faces in the News" labeling in [16], where detected frontal faces in news images are tagged with names appearing in the news story text, they proposed to combine visual cues (face and cloth) and textual cues (subtitle and transcript) for assigning names.…”

Section: Face Retrieval In Videomentioning

confidence: 99%

“…They align the transcripts with subtitles using dynamic time warping to obtain textual annotation, and use visual speaker detection to resolve the ambiguities, i.e., only associating names with face tracks where the face is detected as speaking. A nearest neighbor [26] or SVM [27] classifier, trained on labeled tracks, is used to classify the unlabeled face tracks. Their approach has demonstrated promising performance on three 40 minute episodes of a TV serial.…”

Section: Face Retrieval In Videomentioning

confidence: 99%

Face Recognition and Retrieval in Video

Shan¹

2010

Video Search and Mining

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Abstract. Automatic face recognition has long been established as one of the most active research areas in computer vision. Face recognition in unconstrained environments remains challenging for most practical applications. In contrast to traditional still-image based approaches, recently the research focus has shifted towards videobased approaches. Video data provides rich and redundant information, which can be exploited to resolve the inherent ambiguities of image-based recognition like sensitivity to low resolution, pose variations and occlusion, leading to more accurate and robust recognition. Face recognition has also been considered in the content-based video retrieval setup, for example, character-based video search. In this chapter, we review existing research on face recognition and retrieval in video. The relevant techniques are comprehensively surveyed and discussed.

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Taking the bite out of automated naming of characters in TV video

Cited by 164 publications

References 35 publications

Nature Conservation Drones for Automatic Localization and Counting of Animals

Nature Conservation Drones for Automatic Localization and Counting of Animals

Monitoring Interactions

Face Recognition and Retrieval in Video

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