Temporal activity detection in untrimmed videos with recurrent neural networks
Document typeConference lecture
Rights accessOpen Access
This work proposes a simple pipeline to classify and temporally localize activities in untrimmed videos. Our system uses features from a 3D Convolutional Neural Network (C3D) as input to train a a recurrent neural network (RNN) that learns to classify video clips of 16 frames. After clip prediction, we post-process the output of the RNN to assign a single activity label to each video, and determine the temporal boundaries of the activity within the video. We show how our system can achieve competitive results in both tasks with a simple architecture. We evaluate our method in the ActivityNet Challenge 2016, achieving a 0.5874 mAP and a 0.2237 mAP in the classification and detection tasks, respectively. Our code and models are publicly available at: https://imatge-upc.github.io/activitynet-2016-cvprw/
CitationMontes, A., Salvador, A., Pascual, S., Giro, X. Temporal activity detection in untrimmed videos with recurrent neural networks. A: NIPS Workshop on Large Scale Computer Vision Systems. "Proceedings of the 1st NIPS Workshop on Large Scale Computer Vision Systems". Barcelona: 2016, p. 1-5.