Mask RCNN for Human Motion and Actions Recognition

Zaghbani, Soumaya; Bouhlel, Med Salim

doi:10.1007/978-3-030-73689-7_1

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…SegCodeNet [47] uses a Mask R-CNN [48] to generate instance masks and combines them with the input frames in a two-stream approach to recognize different activities of wearables. The work of [49] uses a Mask R-CNN to extract the instance masks of one single actor in a video sequence and mask the frames accordingly. The masked frames are then fed into a single-stream CNN for action recognition.…”

Section: Related Workmentioning

confidence: 99%

Action Detection for Wildlife Monitoring with Camera Traps Based on Segmentation with Filtering of Tracklets (SWIFT) and Mask-Guided Action Recognition (MAROON)

Schindler,

Steinhage,

van Beeck Calkoen

et al. 2024

Applied Sciences

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Behavioral analysis of animals in the wild plays an important role for ecological research and conservation and has been mostly performed by researchers. We introduce an action detection approach that automates this process by detecting animals and performing action recognition on the detected animals in camera trap videos. Our action detection approach is based on SWIFT (segmentation with filtering of tracklets), which we have already shown to successfully detect and track animals in wildlife videos, and MAROON (mask-guided action recognition), an action recognition network that we are introducing here. The basic ideas of MAROON are the exploitation of the instance masks detected by SWIFT and a triple-stream network. The instance masks enable more accurate action recognition, especially if multiple animals appear in a video at the same time. The triple-stream approach extracts features for the motion and appearance of the animal. We evaluate the quality of our action recognition on two self-generated datasets, from an animal enclosure and from the wild. These datasets contain videos of red deer, fallow deer and roe deer, recorded both during the day and night. MAROON improves the action recognition accuracy compared to other state-of-the-art approaches by an average of 10 percentage points on all analyzed datasets and achieves an accuracy of 69.16% on the Rolandseck Daylight dataset, in which 11 different action classes occur. Our action detection system makes it possible todrasticallyreduce the manual work of ecologists and at the same time gain new insights through standardized results.

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Section: Related Workmentioning

confidence: 99%

Action Detection for Wildlife Monitoring with Camera Traps Based on Segmentation with Filtering of Tracklets (SWIFT) and Mask-Guided Action Recognition (MAROON)

Schindler,

Steinhage,

van Beeck Calkoen

et al. 2024

Applied Sciences

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“…Human activity recognition in a fixed camera-based surveillance system is relatively one of the well-studied areas of interest whereas human activity recognition in UAV-based surveillance systems is comparatively less studied [31], [32], [33]. This area of research has drawn a great deal of attention Needs more categories and samples per category to unveil new challenges in understanding and recognizing human activities in present scenarios Inception-v3 [17] ILSVR 2012 Enhances the kernels of inception module such as convolutional and pooling in GoogleNet into 3D…”

Section: Problem Definitionmentioning

confidence: 99%

Bi-LSTM Model to Recognize Human Activities in UAV Videos using Inflated I3D-ConvNet

Gundu¹,

Hussain²

2022

IJACSA

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Human activity recognition in aerial videos is an emerging research area. In this paper, an Inflated I3D-ConvNet (Inflated I3D) and Bidirectional Long Short-Term Memory (Bi-LSTM) based human action recognition model in UAV videos have been proposed. The initial module was pre-trained using the Kinetics-400 video dataset, which consisted of 400 classes of human activities and around 400 video clips for each class culled from real-world and arduous YouTube videos. The proposed inflated I3D-ConvNet which was built on 2D-ConvNet inflation learns and extracts spatio-temporal features from aerial video while leveraging the architectural design of Inception-V1. The proposed model employs Bi-LSTM architecture for human action classification on the Drone-Action dataset which is a smaller benchmark UAV-captured video dataset. This model considerably improves the state-of-the-art results in activity classification using the SoftMax classifier and retains an accuracy of about 98.4%.

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Multi-task CNN for multi-cue affects recognition using upper-body gestures and facial expressions

Zaghbani

Bouhlel²

2021

Int. j. inf. tecnol.

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Mask RCNN for Human Motion and Actions Recognition

Cited by 4 publications

References 14 publications

Action Detection for Wildlife Monitoring with Camera Traps Based on Segmentation with Filtering of Tracklets (SWIFT) and Mask-Guided Action Recognition (MAROON)

Action Detection for Wildlife Monitoring with Camera Traps Based on Segmentation with Filtering of Tracklets (SWIFT) and Mask-Guided Action Recognition (MAROON)

Bi-LSTM Model to Recognize Human Activities in UAV Videos using Inflated I3D-ConvNet

Multi-task CNN for multi-cue affects recognition using upper-body gestures and facial expressions

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