Abstract
Video-based human activity recognition has fascinated researchers of computer vision community due to its critical challenges and wide variety of applications in surveillance domain. Thus, the development of techniques related to human activity recognition has accelerated. There is now a trend towards implementing deep learning-based activity recognition systems because of performance improvement and automatic feature learning capabilities. This paper implements fusion-based dual-stream deep model for activity recognition with emphasis on minimizing amount of pre-processing required along with fine-tuning of pre-trained model. The architecture is trained and evaluated using standard video actions benchmarks of UCF101. The proposed approach not only provides results comparable with state-of-the-art methods but is also better at exploiting pre-trained model and image data.
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Chandni, Khurana, R., Kushwaha, A.K.S. (2019). Delving Deeper with Dual-Stream CNN for Activity Recognition. In: Khare, A., Tiwary, U., Sethi, I., Singh, N. (eds) Recent Trends in Communication, Computing, and Electronics. Lecture Notes in Electrical Engineering, vol 524. Springer, Singapore. https://doi.org/10.1007/978-981-13-2685-1_32
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DOI: https://doi.org/10.1007/978-981-13-2685-1_32
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