Human activity prediction by mapping grouplets to recurrent Self-Organizing Map

Sun, Quansen; Liu, Hong; Liu, Mengyuan; Zhang, Tianwei

doi:10.1016/j.neucom.2015.11.061

Cited by 8 publications

(6 citation statements)

References 47 publications

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“…A new recognition methodology named dynamic bag of words is developed for the recognition of on-going human activities and interactions. Sun et al [15] detected spatio-temporal interest points and then sparse grouplets are located to represent body parts movement. Wang et al [16] proposed a time series alignment-based activity prediction method.…”

Section: A Handcrafted Featuresmentioning

confidence: 99%

Human Interaction Anticipation by Combining Deep Features and Transformed Optical Flow Components

et al. 2020

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The anticipation of ongoing human interactions is not only highly dynamic and challenging problem but extremely crucial in applications such as remote monitoring, video surveillance, human-robot interaction, anti-terrorists and anti-crime securities. In this work, we address the problem of anticipating the interactions between people monitored by single as well as multiple camera views. To this end, we propose a novel approach that integrates Deep Features with novel hand-crafted features, namely Transformed Optical Flow Components (TOFCs). In order to validate the performance of the proposed approach, we have tested the proposed approach in real outdoor environments, captured using single as well as multiple cameras, having shadow and illumination variations as well as cluttered backgrounds. The results of the proposed approach are also compared with the state-of-the-art approaches. The experimental results show that the proposed approach is promising to anticipate real human interactions.

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Section: A Handcrafted Featuresmentioning

confidence: 99%

Human Interaction Anticipation by Combining Deep Features and Transformed Optical Flow Components

et al. 2020

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“…A human activity prediction [6] method was proposed which predicted human activity by mapping grouplets to Self-Organizing Map (SOM). The detection efficiency was improved by extracting dense spatio-temporal interest points (STIPs) from streaming videos as low level descriptors.…”

Section: Literature Surveymentioning

confidence: 99%

“…Kalman filter is used for simultaneous prediction and filtering of measurement of object position and motion. The standard Kalman filter trying to estimate the state of detected objects in which transition is from to can be expressed as, (6) with a measurement that is,…”

Section: A Enhanced Object Detection and Tracking-spatio-temporal Obmentioning

confidence: 99%

An Enhanced Human Activity Prediction at Rainy and Night Times

D*¹,

Radha²

2019

IJRTE

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Human activity prediction aims to recognize an unfinished activity with limited motion and appearance information. A generalized activity prediction framework was proposed for human activity prediction where Probabilistic Suffix Tree (PST) was introduced to model casual relationships between constituent actions. Then, each kind of activity in videos was predicted by modeling interactive object information through Spatial Pattern Mining (SPM). This framework mined the temporal sequence patterns. For efficient human activity prediction a Spatio-Temporal Frequent Object Mining (STOM) was proposed in which the spatial, size and motion correlation among objects information were collected along with the temporal information. After the collection of this information, the objects were identified by using Modified Histogram Of Gradient (MHOG) and then the objects were tracked by particle filter technique. The frequent action of detected objects were identified by using Frequent Pattern-growth (FP-growth) which predicted the infrequent action as abnormal human activity in videos. However, MHOG based Object Detection and Tracking-STOM (MHOGODT-STFOM) based human activity prediction is not more effective at night time and rainy time. So in this paper, Enhanced Object Detection and Tracking- STFOM (EODTSTFOM) and Removing Rain Streaks-EODT-STFOM (RSREODT-STFOM) are proposed for human activity prediction even at night time and rainy time. In EODT, a modified Contrast Model is used which combined the contrast information and local entropy information to detect object contents present in the current image frame. Then, the objects are tracked by Kalman filter. In RSR-EODT, the rain streaks in the images are removed based on the deep Convolutional Neural Network (CNN). Then the objects are detected and tracked by modified Contrast Model and Kalman filter respectively. After the object detection and object tracking by EODT and RSR-EODT, the frequent actions are obtained by applying STFOM. The frequent actions are considered as normal activities and the infrequent actions are considered as abnormal activities. Thus the proposed EODTSTFOM and RSR-EODT- STFOM methods predict the human activities even at night time and rainy time.

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“…These networks are especially indicated for the visualization, clustering and abstraction of data (Kohonen et al, 2001) given their ability to graphically present information in an orderly way (Haroz & Whitney, 2012;Hofmann et al, 2012) following this pattern, they have a suitability for use in the field of education (Thuneberg & Hotulainen, 2006) given their capacity for grouping and identifying groups. They are also suitable for use in sports due to their ability to predict results (Schöllhorn et al, 2014;Sun et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Analysis of stress and academic-sports commitment through Self-organizing Artificial Neural Networks (Análisis del estrés y el compromiso académico-deportivo mediante Redes Neuronales Artificiales Auto-organizativas)

Bellod¹,

Ramón²,

Carballeira³

et al. 2021

Retos

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Objective: The objective of this research was to understand the relationships between the variables resilience, coping, commitment and stress in the academic and sports fields, and their differences based on varied personal typologies. Material and Methods: A selective research methodology was used, with a retrospective ex-post-facto design. The sample was made up of 190 men between the ages of 13 and 17, athletes and students of Compulsory Secondary Education (ESO), Baccalaureate and Middle Grade Vocational Training. Results: The artificial intelligence technique SOM, allowed to identify in an autonomous way three clusters of athletes with psychological and sociodemographic characteristics similar to each other and different from those of the rest. Task coping was positively related to resilience and commitment and negatively to stress whereas emotion-oriented coping was positively related to stress. Conclusions: Athlete students tend to use the same coping strategies when facing stressful situations that they use in their sports practice. The levels in the variables analysed depend on the type of student and athlete, which can be used to identify risk groups and carry out interventions for their improvement. Resumen. Objetivo: El objetivo de esta investigación fue conocer las relaciones entre las variables resiliencia, afrontamiento, compromiso y estrés en el ámbito académico y deportivo, y sus diferencias en función de diferentes tipologías personales. Material y Métodos: Se empleó una metodología de investigación selectiva, con un diseño ex-post-facto retrospectivo. La muestra estuvo formada por 190 varones de edades comprendidas entre los 13 y los 17 años, deportistas y estudiantes de Educación Secundaria Obligatoria (ESO), Bachillerato y Formación Profesional de Grado Medio. Resultados: La técnica de inteligencia artificial SOM, permitió identificar de un modo autónomo tres clústeres de deportistas con características psicológicas y sociodemográficas semejantes entre sí y distintas a los del resto. El afrontamiento de tarea se relacionó positivamente con la resiliencia y el compromiso y negativamente con el estrés y el afrontamiento centrado en la emoción se relacionó positivamente con el estrés. Conclusiones: Los estudiantes deportistas tienden a emplear en el entorno académico las mismas estrategias de afrontamiento ante situaciones de estrés que usan en su práctica deportiva. Los niveles en las variables analizadas dependen de la tipología de estudiante y deportista, lo cual puede servir para identificar grupos de riesgo y realizar intervenciones para su mejora.

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Human activity prediction by mapping grouplets to recurrent Self-Organizing Map

Cited by 8 publications

References 47 publications

Human Interaction Anticipation by Combining Deep Features and Transformed Optical Flow Components

Human Interaction Anticipation by Combining Deep Features and Transformed Optical Flow Components

An Enhanced Human Activity Prediction at Rainy and Night Times

Analysis of stress and academic-sports commitment through Self-organizing Artificial Neural Networks (Análisis del estrés y el compromiso académico-deportivo mediante Redes Neuronales Artificiales Auto-organizativas)

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