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Understanding human behavior and activity facilitates advancement of numerous real-world applications, and is critical for video analysis. Despite the progress of action recognition algorithms in trimmed videos, the majority of real-world videos are lengthy and untrimmed with sparse segments of interest. The task of temporal activity detection in untrimmed videos aims to localize the temporal boundary of actions and classify the action categories. Temporal activity detection task has been investigated in full and limited supervision settings depending on the availability of action annotations. This paper provides an extensive overview of deep learning-based algorithms to tackle temporal action detection in untrimmed videos with different supervision levels including fully-supervised, weakly-supervised, unsupervised, self-supervised, and semi-supervised. In addition, this paper reviews advances in spatio-temporal action detection where actions are localized in both temporal and spatial dimensions. Action detection in online setting is also reviewed where the goal is to detect actions in each frame without considering any future context in a live video stream. Moreover, the commonly used action detection benchmark datasets and evaluation metrics are described, and the performance of the state-of-the-art methods are compared. Finally, real-world applications of temporal action detection in untrimmed videos and a set of future directions are discussed.
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Learning to Anonymize Faces for Privacy Preserving Action Detection
There is an increasing concern in computer vision devices invading the privacy of their users. We want the camera systems/robots to recognize important events and assist human daily life by understanding its videos, but we also want to ensure that they do not intrude people's privacy. In this paper, we propose a new principled approach for learning a video anonymizer. We use an adversarial training setting in which two competing systems fight: (1) a video anonymizer that modifies the original video to remove privacy-sensitive information (i.e., human face) while still trying to maximize spatial action detection performance, and (2) a discriminator that tries to extract privacy-sensitive information from such anonymized videos. The end goal is for the video anonymizer to perform a pixel-level modification of video frames to anonymize each person's face, while minimizing the effect on action detection performance. We experimentally confirm the benefit of our approach particularly compared to conventional hand-crafted video/face anonymization methods including masking, blurring, and noise adding.
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- Award ID(s):
- 1748387
- PAR ID:
- 10100521
- Date Published:
- Journal Name:
- European Conference on Computer Vision (ECCV)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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