7,322 research outputs found
Linear-time Online Action Detection From 3D Skeletal Data Using Bags of Gesturelets
Sliding window is one direct way to extend a successful recognition system to
handle the more challenging detection problem. While action recognition decides
only whether or not an action is present in a pre-segmented video sequence,
action detection identifies the time interval where the action occurred in an
unsegmented video stream. Sliding window approaches for action detection can
however be slow as they maximize a classifier score over all possible
sub-intervals. Even though new schemes utilize dynamic programming to speed up
the search for the optimal sub-interval, they require offline processing on the
whole video sequence. In this paper, we propose a novel approach for online
action detection based on 3D skeleton sequences extracted from depth data. It
identifies the sub-interval with the maximum classifier score in linear time.
Furthermore, it is invariant to temporal scale variations and is suitable for
real-time applications with low latency
Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks
This paper addresses the problem of continuous gesture recognition from
sequences of depth maps using convolutional neutral networks (ConvNets). The
proposed method first segments individual gestures from a depth sequence based
on quantity of movement (QOM). For each segmented gesture, an Improved Depth
Motion Map (IDMM), which converts the depth sequence into one image, is
constructed and fed to a ConvNet for recognition. The IDMM effectively encodes
both spatial and temporal information and allows the fine-tuning with existing
ConvNet models for classification without introducing millions of parameters to
learn. The proposed method is evaluated on the Large-scale Continuous Gesture
Recognition of the ChaLearn Looking at People (LAP) challenge 2016. It achieved
the performance of 0.2655 (Mean Jaccard Index) and ranked place in
this challenge
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