Fine-grained Activity Recognition with Holistic and Pose based Features

by   Leonid Pishchulin, et al.

Holistic methods based on dense trajectories are currently the de facto standard for recognition of human activities in video. Whether holistic representations will sustain or will be superseded by higher level video encoding in terms of body pose and motion is the subject of an ongoing debate. In this paper we aim to clarify the underlying factors responsible for good performance of holistic and pose-based representations. To that end we build on our recent dataset leveraging the existing taxonomy of human activities. This dataset includes 24,920 video snippets covering 410 human activities in total. Our analysis reveals that holistic and pose-based methods are highly complementary, and their performance varies significantly depending on the activity. We find that holistic methods are mostly affected by the number and speed of trajectories, whereas pose-based methods are mostly influenced by viewpoint of the person. We observe striking performance differences across activities: for certain activities results with pose-based features are more than twice as accurate compared to holistic features, and vice versa. The best performing approach in our comparison is based on the combination of holistic and pose-based approaches, which again underlines their complementarity.


Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data

Activity recognition has shown impressive progress in recent years. Howe...

ReHAR: Robust and Efficient Human Activity Recognition

Designing a scheme that can achieve a good performance in predicting sin...

Disparity-Augmented Trajectories for Human Activity Recognition

Numerous methods for human activity recognition have been proposed in th...

Follow the Attention: Combining Partial Pose and Object Motion for Fine-Grained Action Detection

Activity recognition in shopping environments is an important and challe...

Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data

Previous work has demonstrated that virtual accelerometry data, extracte...

Unstructured Human Activity Detection from RGBD Images

Being able to detect and recognize human activities is essential for sev...

Overcoming the Domain Gap in Neural Action Representations

Relating animal behaviors to brain activity is a fundamental goal in neu...

Please sign up or login with your details

Forgot password? Click here to reset