Title:Deep Learning Trends for Video Based Activity Recognition: A Survey
VOLUME: 8 ISSUE: 3
Author(s): Chandni, Alok Kumar Singh Kushwaha* and Jagwinder Kaur Dhillon
Affiliation:Department of Computer Science and Engineering, IKGPTU, Kapurthala, Department of Computer Science Engineering, IKGPTU, Kapurthala, Department of Computer Science Engineering, IKGPTU, Kapurthala
Keywords:Activity recognition, convolution, deep learning, handcrafted features, spatio-temporal dimension,
algorithams.
Abstract:Background and Objective: Video-based human activity recognition is a prominent area of
research due to a wide range of applications from intelligent video surveillance to human-computer
interaction. Recent work on video analysis is focused on applying deep learning approach to accomplish
the task of activity recognition.
Conclusion: Deep networks can dramatically improve the recognition performance because of its
hierarchical nature to exploit the video frame structure in reducing the search space of the learning
model. This motivated us to provide a comprehensive survey of the state-of-art deep models for recognizing
human actions/activities.
