1,152 research outputs found
Vision-based Human Fall Detection Systems using Deep Learning: A Review
Human fall is one of the very critical health issues, especially for elders
and disabled people living alone. The number of elder populations is increasing
steadily worldwide. Therefore, human fall detection is becoming an effective
technique for assistive living for those people. For assistive living, deep
learning and computer vision have been used largely. In this review article, we
discuss deep learning (DL)-based state-of-the-art non-intrusive (vision-based)
fall detection techniques. We also present a survey on fall detection benchmark
datasets. For a clear understanding, we briefly discuss different metrics which
are used to evaluate the performance of the fall detection systems. This
article also gives a future direction on vision-based human fall detection
techniques
Deep Architectures and Ensembles for Semantic Video Classification
This work addresses the problem of accurate semantic labelling of short
videos. To this end, a multitude of different deep nets, ranging from
traditional recurrent neural networks (LSTM, GRU), temporal agnostic networks
(FV,VLAD,BoW), fully connected neural networks mid-stage AV fusion and others.
Additionally, we also propose a residual architecture-based DNN for video
classification, with state-of-the art classification performance at
significantly reduced complexity. Furthermore, we propose four new approaches
to diversity-driven multi-net ensembling, one based on fast correlation measure
and three incorporating a DNN-based combiner. We show that significant
performance gains can be achieved by ensembling diverse nets and we investigate
factors contributing to high diversity. Based on the extensive YouTube8M
dataset, we provide an in-depth evaluation and analysis of their behaviour. We
show that the performance of the ensemble is state-of-the-art achieving the
highest accuracy on the YouTube-8M Kaggle test data. The performance of the
ensemble of classifiers was also evaluated on the HMDB51 and UCF101 datasets,
and show that the resulting method achieves comparable accuracy with
state-of-the-art methods using similar input features
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