Behavior identification method based on 3D point cloud and key bone nodes

A 3D and node technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as ignoring geometry and topology, camera angle, sensitivity to actor speed changes, and low recognition rate of complex behaviors. Behavior recognition accuracy and the effect of improving recognition accuracy

Active Publication Date: 2016-08-31
HUNAN UNIV
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

These methods have the following three defects: first, they are sensitive to lighting, camera angles, background changes and partial occlusion; second, they ignore the inherent geometry and topology of the behavior itself; Difficult to identify trade-offs
However, using depth picture Most of the feature extraction methods are similar to RGB, sensitive to changes in camera angle and actor speed, and the recognition rate of complex behaviors involving human-object interaction is not high

Method used

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  • Behavior identification method based on 3D point cloud and key bone nodes
  • Behavior identification method based on 3D point cloud and key bone nodes
  • Behavior identification method based on 3D point cloud and key bone nodes

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0019] Action Recognition on the MSR-Action 3D Dataset. The MSR-Action 3D data set contains 20 behaviors, namely: high arm swing, cross arm swing, hammering, grasping, forward boxing, high throwing, drawing X, drawing hook, drawing circle, clapping hands, swinging with both hands , Side boxing, bending, forward kicking, side kicking, jogging, tennis swing, tennis serve, golfing, picking up, and throwing, each act is performed 2 or 3 times by 10 people. The actors collected in this database are in a fixed position, and most of the behaviors mainly involve the movement of the upper body of the actor. First, we directly extract the 3D point cloud sequence from the depth sequence, and divide the 3D point cloud sequence into non-overlapping (24×32×18) and (24×32×12) along the X, Y, and T directions respectively. spatio-temporal units; our method is then tested using cross-validation, i.e. five subjects for training and the remaining five for testing, exhaustively 252 times. Table...

Embodiment 2

[0021]Action Recognition on the MSR Daily Activity 3D Dataset. The data set contains 16 behaviors, consisting of 10 behavior subjects, each behavior subject performs the behavior 2 times, one standing, one sitting, a total of 320 behavior videos. The 16 behaviors are: drinking, eating, reading, talking on the phone, writing, sitting, using a notebook, vacuum cleaning, laughing, throwing paper, playing games, lying on the sofa, walking, playing guitar, standing, and sitting. The experimental setup is the same as above, this database is extremely challenging, not only contains intra-class variation, but also involves human-object interaction behavior. Table 2 is a comparison of the recognition rates of different methods on this database. It can be seen from the table that our method has achieved an accuracy rate of 98.1%, and the average accuracy rate has reached 94.0±5.68%, which is an excellent experimental result.

Embodiment 3

[0023] Action Recognition on the MSR Action Pairs 3D Dataset. This data set is a data set of behavior pairs, including 12 behaviors and 6 groups of behavior pairs, namely: pick up a box, put down a box, lift a box, place a box, push a chair, pull a chair , put on a hat, take off a hat, carry a backpack, take off a backpack, stick a poster, pull a poster. In this database, there are similar motion and shape cues between each behavior pair, but their temporal correlation is opposite. The experimental settings are the same as above. Table 3 is the comparison of all existing popular methods on this database. Our method has achieved a recognition rate of 97.2%.

[0024] Table 1: Performance of existing methods on the MSR Action 3D dataset. Mean±STD is calculated from 252 cycles.

[0025] The 5 / 5 column means that {1,3,5,7,9} of the agents are used for training and the rest are used for testing.

[0026]

[0027] Table 2: Comparison of MSR Daily Activity recognition rates. M...

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Abstract

The invention relates to a behavior identification method based on 3D point cloud and key bone nodes. The method particularly comprises steps that firstly, a 3D point cloud sequence is extracted from a depth map, the 3D point cloud sequence is uniformly divided into N non-overlapped space-time units, a local position model (LPP) of each space-time unit is calculated, and local position model statistics deviation descriptors (SDLPP) are calculated; secondly, a key bone node subset is extracted from the 3D bone nodes by utilizing a node exercise amount algorithm, 3D node position characteristics of the key bone nodes and a local occupation model (LOP) in the corresponding depth map are calculated; lastly, the three heterogeneous characteristics are cascaded, and random determination forest excavation distinguishable characteristics are utilized to realize classification and behavior identification. According to the method, the 3D local geometric characteristics and dynamic time characteristics of human body behaviors are extracted, an identification rate of complex human body behaviors related to man-object interaction is relatively high, and the method is suitable for complex human body behavior identification.

Description

technical field [0001] The invention belongs to the fields of artificial intelligence and pattern recognition, and in particular relates to behavior recognition based on 3D point clouds and key skeletal nodes. Background technique [0002] Human behavior recognition mainly refers to the analysis and recognition of the action types and behavior patterns of the observed people, and uses natural language to describe them. According to the complexity of behavior, some researchers divide human behavior into four levels: posture, individual behavior, interactive behavior, and group behavior. At present, most of the researches are still mainly focused on the first two levels, while there are relatively few research reports on the latter two levels. Human behavior recognition technology has broad application prospects and very considerable economic value. The application fields involved mainly include: video surveillance, medical diagnosis and monitoring, motion analysis, intellige...

Claims

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V40/20
Inventor 张汗灵
Owner HUNAN UNIV
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