Abstract
Human activity recognition is one of the most challenging and active areas of research in the computer vision domain. However, designing automatic systems that are robust to significant variability due to object combinations and the high complexity of human motions are more challenging. In this paper, we propose to model the inter-frame rigid evolution of skeleton parts as the trajectory in the Lie group SE(3)×…×SE(3). The motion of the object is similarly modeled as an additional trajectory in the same manifold. The classification is performed based on a rate-invariant comparison of the resulting trajectories mapped to a vector space, the Lie algebra. Experimental results on three action and activity datasets show that the proposed method outperforms various state-of-the-art human activity recognition approaches.
Highlights
Human activity recognition has attracted many research groups in recent years due to its wide range of promising applications in different domains, like surveillance, video games, physical rehabilitation, etc
We propose a framework for human activity recognition using the body part-based skeleton for action recognition and object detection and object tracking for human–object interaction recognition
In order to validate our method, an evaluation was conducted on three databases that represent different challenges, namely Microsoft Research (MSR) Action3D dataset [8], MSR-Daily Activity
Summary
Human activity recognition has attracted many research groups in recent years due to its wide range of promising applications in different domains, like surveillance, video games, physical rehabilitation, etc. The introduction of low cost depth cameras with real-time capabilities, like the Microsoft Kinect, which provide in addition to the classical red-green-blue (RGB) image, a depth image, makes it possible to estimate in real time a 3D humanoid skeleton thanks to the work of Shotton et al [1]. This type of data brings several advantages as it makes the background easy to remove and allows extracting and tracking the human body, capturing the human motion in each frame.
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