Human Action Recognition with RGB-D Sensors

Cippitelli, Enea; Gambi, Ennio; Spinsante, Susanna

doi:10.5772/68121

Cited by 6 publications

(2 citation statements)

References 77 publications

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“…Cippitelli et al [18] used skeleton data collected from RGBD sensors for action recognition. Yet another study by Cippitelli et al [19] is based on RGBD for human action recognition. Ribono and Bettini [7] proposed an ontology-based solution based on hybrid reasoning and contextaware approaches.…”

Section: Privacy Aware Action Recognitionmentioning

confidence: 99%

A multi-task learning based hybrid prediction algorithm for privacy preserving human activity recognition framework

Kumar¹,

Jonnadula²

2021

Bulletin EEI

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There is ever increasing need to use computer vision devices to capture videos as part of many real-world applications. However, invading privacy of people is the cause of concern. There is need for protecting privacy of people while videos are used purposefully based on objective functions. One such use case is human activity recognition without disclosing human identity. In this paper, we proposed a multi-task learning based hybrid prediction algorithm (MTL-HPA) towards realising privacy preserving human activity recognition framework (PPHARF). It serves the purpose by recognizing human activities from videos while preserving identity of humans present in the multimedia object. Face of any person in the video is anonymized to preserve privacy while the actions of the person are exposed to get them extracted. Without losing utility of human activity recognition, anonymization is achieved. Humans and face detection methods file to reveal identity of the persons in video. We experimentally confirm with joint-annotated human motion data base (JHMDB) and daily action localization in YouTube (DALY) datasets that the framework recognises human activities and ensures non-disclosure of privacy information. Our approach is better than many traditional anonymization techniques such as noise adding, blurring, and masking.

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Section: Privacy Aware Action Recognitionmentioning

confidence: 99%

A multi-task learning based hybrid prediction algorithm for privacy preserving human activity recognition framework

Kumar¹,

Jonnadula²

2021

Bulletin EEI

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“…Starting from the multimodal output of the Microsoft Kinect system, many sets of features of different nature (color-, depth- and skeleton-based) are used to train the classification models for HAR. To these aims, features usually range from RGB images, depth-based global features such as space-time volume, and silhouette information, to motion kinematic skeleton descriptors such as joint position and motion (velocity and acceleration), joint distances, joint angles, 3D relative geometric relationships between rigid body parts [ 40 , 41 , 42 ]. The possibility to fuse the different multimodal information obtained by RGB-D cameras has been recently explored giving good results [ 43 , 44 ].…”

Section: Introductionmentioning

confidence: 99%

Neural Networks for Automatic Posture Recognition in Ambient-Assisted Living

Guerra

Schmid

Beltrami

et al. 2022

Sensors

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Human Action Recognition (HAR) is a rapidly evolving field impacting numerous domains, among which is Ambient Assisted Living (AAL). In such a context, the aim of HAR is meeting the needs of frail individuals, whether elderly and/or disabled and promoting autonomous, safe and secure living. To this goal, we propose a monitoring system detecting dangerous situations by classifying human postures through Artificial Intelligence (AI) solutions. The developed algorithm works on a set of features computed from the skeleton data provided by four Kinect One systems simultaneously recording the scene from different angles and identifying the posture of the subject in an ecological context within each recorded frame. Here, we compare the recognition abilities of Multi-Layer Perceptron (MLP) and Long-Short Term Memory (LSTM) Sequence networks. Starting from the set of previously selected features we performed a further feature selection based on an SVM algorithm for the optimization of the MLP network and used a genetic algorithm for selecting the features for the LSTM sequence model. We then optimized the architecture and hyperparameters of both models before comparing their performances. The best MLP model (3 hidden layers and a Softmax output layer) achieved 78.4%, while the best LSTM (2 bidirectional LSTM layers, 2 dropout and a fully connected layer) reached 85.7%. The analysis of the performances on individual classes highlights the better suitability of the LSTM approach.

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