Timed-image based deep learning for action recognition in video sequences

Atto, Abdourrahmane M.; Benoît, Alexandre; Lambert, Patrick

doi:10.1016/j.patcog.2020.107353

Cited by 22 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…196 In Ref. 197 the authors summarized some techniques and studies used to recognize video sequence actions from timed images. The authors presented in Ref.…”

Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

View full text Add to dashboard Cite

Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem is initially formulated along with its mathematical fundamentals. Then, the most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations. Particular attention is given to feed forward networks, recurrent neural networks (including Elman, long-short term memory, gated recurrent units, and bidirectional networks), and convolutional neural networks. Practical aspects, such as the setting of values for hyperparameters and the choice of the most suitable frameworks, for the successful application of deep learning to time series are also provided and discussed. Several fruitful research fields in which the architectures analyzed have obtained a good performance are reviewed. As a result, research gaps have been identified in the literature for several domains of application, thus expecting to inspire new and better forms of knowledge.

show abstract

“…196 In Ref. 197 the authors summarized some techniques and studies used to recognize video sequence actions from timed images. The authors presented in Ref.…”

Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Average recognition rate (%) e proposed method 93.42 Afrasiabi et al [56] 88.43 Atto et al [57] 89.12 Yoon et al [58] 87.71 Wemlinger and Holder [59] 90.12 Bagautdinov et al [60] 88.23 Hussain et al [61] 91.13 Khan et al [62] 90.33 [56] 89.61 Atto et al [57] 90.18 Yoon et al [58] 89.66 Zachary and Holder [59] 88.24 Bagautdinov et al [60] 87.22 Hussain et al [61] 88.89 Khan et al [62] 90.13 [56] 90.16 Atto et al [57] 91.13 Yoon et al [58] 89.32 Zachary and Holder [59] 90.18 Bagautdinov et al [60] 89.85 Hussain et al [61] 91.76 Khan et al [62] 90.11 [56] 90.20 Atto et al [57] 91.22 Yoon et al [58] 90.16 Zachary and Holder [59] 89.19 Bagautdinov et al [60] 90.17 Hussain et al [61] 90.31 Khan et al [62] 88.17 behaviors. We build a flexible and efficient model of the multiperson interaction relation estimation framework to capture the appearance and position relation of people, and a new reasoning approach is thus formed.…”

Section: Methodsmentioning

confidence: 99%

Multiperson Interactive Activity Recognition Based on Interaction Relation Model

Chi

et al. 2021

Journal of Mathematics

View full text Add to dashboard Cite

Multiperson activity recognition is a pivotal branch as well as a challenging topic of human action recognition research. This paper adopts a hybrid learning model to the spatio-temporal relationship and occlusion relationship among multiple people. Initially, this paper builds up an active multiperson interaction relationship estimation framework model to capture interpersonal spatio-temporal relation. This model incorporates the interaction relationship estimation framework with the multiperson relationship network. On this ground, it automatically learns from the human-computer interaction dataset in an end-to-end manner and performs reasoning with standard matrix operations. Secondly, this paper proposed an adaptive occlusion state behavior recognition method derived from the semantic knowledge model to ravel out the concern of occlusion and self-occlusion in human action recognition. Then, Petri Nets are used to recognize multiperson interactive actions. This model has been through extensive experiments on the TV interaction dataset, Vlog dataset, AVA dataset, and MLB-YouTube dataset, experimental results have proved that the recognition performance of this model is superior than the other available models. This paper prospects and summarizes the estimation framework of the interaction relationship and occlusion semantic-knowledge relationship. Experimental results suggest that the proposed method in the paper could capture the discriminative relation information for multiperson interactive activity recognition, which further validates the efficiency of the hybrid learning model.

show abstract

“…However, their proposed model could not detect objects with accuracy if it had high occlusion under different lighting conditions. An action recognition method is used for detecting the anomaly using timed image-based CNN [ 27 ].…”

Section: Related Workmentioning

confidence: 99%

Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities

Ingle

Kim

2022

Sensors

View full text Add to dashboard Cite

With the adaptation of video surveillance in many areas for object detection, monitoring abnormal behavior in several cameras requires constant human tracking for a single camera operative, which is a tedious task. In multiview cameras, accurately detecting different types of guns and knives and classifying them from other video surveillance objects in real-time scenarios is difficult. Most detecting cameras are resource-constrained devices with limited computational capacities. To mitigate this problem, we proposed a resource-constrained lightweight subclass detection method based on a convolutional neural network to classify, locate, and detect different types of guns and knives effectively and efficiently in a real-time environment. In this paper, the detection classifier is a multiclass subclass detection convolutional neural network used to classify object frames into different sub-classes such as abnormal and normal. The achieved mean average precision by the best state-of-the-art framework to detect either a handgun or a knife is 84.21% or 90.20% on a single camera view. After extensive experiments, the best precision obtained by the proposed method for detecting different types of guns and knives was 97.50% on the ImageNet dataset and IMFDB, 90.50% on the open-image dataset, 93% on the Olmos dataset, and 90.7% precision on the multiview cameras. This resource-constrained device has shown a satisfactory result, with a precision score of 85.5% for detection in a multiview camera.

show abstract

Timed-image based deep learning for action recognition in video sequences

Cited by 22 publications

References 21 publications

Deep Learning for Time Series Forecasting: A Survey

Deep Learning for Time Series Forecasting: A Survey

Multiperson Interactive Activity Recognition Based on Interaction Relation Model

Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities

Contact Info

Product

Resources

About