Hidden Two-Stream Convolutional Networks for Action Recognition

Analyzing videos of human actions involves understanding the temporal relationships among video frames. State-of-the-art action recognition approaches rely on traditional optical flow estimation methods to pre-compute motion information for CNNs. Such a two-stage approach is computationally expensive, storage demanding, and not end-to-end trainable. In this paper, we present a novel CNN architecture that implicitly captures motion information between adjacent frames. We name our approach hidden two-stream CNNs because it only takes raw video frames as input and directly predicts action classes without explicitly computing optical flow. Our end-to-end approach is 10x faster than its two-stage baseline. Experimental results on four challenging action recognition datasets: UCF101, HMDB51, THUMOS14 and ActivityNet v1.2 show that our approach significantly outperforms the previous best real-time approaches.Comment: Accepted at ACCV 2018, camera ready. Code available at https://github.com/bryanyzhu/Hidden-Two-Strea

Large-Scale Mapping of Human Activity using Geo-Tagged Videos

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to accurately map activities both spatially and temporally. We also demonstrate the advantages of using the visual content over the tags/titles.Comment: Accepted at ACM SIGSPATIAL 201

Spatio-Temporal Fusion Networks for Action Recognition

The video based CNN works have focused on effective ways to fuse appearance and motion networks, but they typically lack utilizing temporal information over video frames. In this work, we present a novel spatio-temporal fusion network (STFN) that integrates temporal dynamics of appearance and motion information from entire videos. The captured temporal dynamic information is then aggregated for a better video level representation and learned via end-to-end training. The spatio-temporal fusion network consists of two set of Residual Inception blocks that extract temporal dynamics and a fusion connection for appearance and motion features. The benefits of STFN are: (a) it captures local and global temporal dynamics of complementary data to learn video-wide information; and (b) it is applicable to any network for video classification to boost performance. We explore a variety of design choices for STFN and verify how the network performance is varied with the ablation studies. We perform experiments on two challenging human activity datasets, UCF101 and HMDB51, and achieve the state-of-the-art results with the best network

Texture-Based Input Feature Selection for Action Recognition

The performance of video action recognition has been significantly boosted by using motion representations within a two-stream Convolutional Neural Network (CNN) architecture. However, there are a few challenging problems in action recognition in real scenarios, e.g., the variations in viewpoints and poses, and the changes in backgrounds. The domain discrepancy between the training data and the test data causes the performance drop. To improve the model robustness, we propose a novel method to determine the task-irrelevant content in inputs which increases the domain discrepancy. The method is based on a human parsing model (HP model) which jointly conducts dense correspondence labelling and semantic part segmentation. The predictions from the HP model also function as re-rendering the human regions in each video using the same set of textures to make humans appearances in all classes be the same. A revised dataset is generated for training and testing and makes the action recognition model exhibit invariance to the irrelevant content in the inputs. Moreover, the predictions from the HP model are used to enrich the inputs to the AR model during both training and testing. Experimental results show that our proposed model is superior to existing models for action recognition on the HMDB-51 dataset and the Penn Action dataset