DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

Nogas, Jacob; Khan, Shehroz S.; Mihailidis, Alex

doi:10.48550/arxiv.1809.00977

Cited by 2 publications

(5 citation statements)

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“…DAE [20] 75.0 CAE [20] 76.0 CLSTMAE [27] 82.0 DSTCAE [26] 89.0 r-VAE 90. -UR fall [12]: This dataset contains 70 depth videos collected with a Microsoft Kinect camera in a nursing home.…”

Section: Methods Auc (%)mentioning

confidence: 99%

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Few-shot Scene-adaptive Anomaly Detection

Reddy

et al. 2020

Preprint

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We address the problem of anomaly detection in videos. The goal is to identify unusual behaviours automatically by learning exclusively from normal videos. Most existing approaches are usually datahungry and have limited generalization abilities. They usually need to be trained on a large number of videos from a target scene to achieve good results in that scene. In this paper, we propose a novel few-shot scene-adaptive anomaly detection problem to address the limitations of previous approaches. Our goal is to learn to detect anomalies in a previously unseen scene with only a few frames. A reliable solution for this new problem will have huge potential in real-world applications since it is expensive to collect a massive amount of data for each target scene. We propose a meta-learning based approach for solving this new problem; extensive experimental results demonstrate the effectiveness of our proposed method. All codes are released in https://github.com/yiweilu3/Few-shot-Scene-adaptive-Anomaly-Detection.

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“…DAE [20] 75.0 CAE [20] 76.0 CLSTMAE [27] 82.0 DSTCAE [26] 89.0 r-VAE 90. -UR fall [12]: This dataset contains 70 depth videos collected with a Microsoft Kinect camera in a nursing home.…”

Section: Methods Auc (%)mentioning

confidence: 99%

“…This dataset is originally collected for research in fall detection. We follow previous work in [26] which considers a person falling as the anomaly. Again, we use this dataset for testing.…”

Section: Methods Auc (%)mentioning

confidence: 99%

Few-shot Scene-adaptive Anomaly Detection

Reddy

et al. 2020

Preprint

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“…The proposed frame also differs from the work of Lee et al [9] in that it uses a 3DCAE instead of the bi-directional convolutional LSTM or 2D CAE. The work of Nogas et al [13] suggests that training LSTM based autoencoders can be very slower in comparison to 3DCAE. Our 3DCAE reconstructs the whole sequence of frames given an input sequence of frames instead of producing only one frame and is fed to the 3DCNN discriminator.…”

Section: Related Workmentioning

confidence: 99%

“…The spatio-temporal framework is trained in an adversarial manner on only normal ADL and an unseen fall is detected as an anomaly during testing. The strategy to detect unseen falls is shown in Figure 2 (derived from [13]). All the frames in the video, F r i , are broken down into windows of frames of length, T = 8, with stride=1.…”

Section: Detecting Unseen Fallsmentioning

confidence: 99%

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Spatio-Temporal Adversarial Learning for Detecting Unseen Falls

Khan,

Nogas,

Mihailidis

2019

Preprint

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Fall detection is an important problem from both the health and machine learning perspective. A fall can lead to severe injuries, long term impairments or even death in some cases. In terms of machine learning, it presents a severely class imbalance problem with very few or no training data for falls owing to the fact that falls occur rarely. In this paper, we take an alternate philosophy to detect falls in the absence of their training data, by training the classifier on only the normal activities (that are available in abundance) and identifying a fall as an anomaly. To realize such a classifier, we use an adversarial learning framework, which comprises of a spatio-temporal autoencoder for reconstructing input video frames and a spatio-temporal convolution network to discriminate them against original video frames. 3D convolutions are used to learn spatial and temporal features from the input video frames. The adversarial learning of the spatio-temporal autoencoder will enable reconstructing the normal activities of daily living efficiently; thus, rendering detecting unseen falls plausible within this framework. We tested the performance of the proposed framework on camera sensing modalities that may preserve an individual's privacy (fully or partially), such as thermal and depth camera. Our results on three publicly available datasets show that the proposed spatio-temporal adversarial framework performed better than other frame based (or spatial) adversarial learning methods.

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DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders

Cited by 2 publications

References 0 publications

Few-shot Scene-adaptive Anomaly Detection

Few-shot Scene-adaptive Anomaly Detection

Spatio-Temporal Adversarial Learning for Detecting Unseen Falls

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