Asymptotic Dynamic Time Warping calculation with utilizing value repetition

Sharabiani, Anooshiravan; Darabi, Houshang; Harford, Samuel; Douzali, Elnaz; Karim, Fazle; Johnson, Hereford; Chen, Shun

doi:10.1007/s10115-018-1163-4

Cited by 21 publications

(16 citation statements)

References 24 publications

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“…However, in time series classification, the general consensus is on the use of 1-nearest neighbor classifiers and its variants to classify time series. [6,7,23,26,36] While the above algorithm has currently been applied to convert the 1NN-DTW distance matrix, it can also be applied to normalize any distance matrix utilized for 1-nearest neighbors classification algorithms. Figures 2 and 3 depict the results from white-box attacks on 1-NN DTW and FCN that is applied on 42 UCR datasets.…”

Section: A Experimentsmentioning

confidence: 99%

Adversarial Attacks on Time Series

Karim

Majumdar

Darabi

2021

IEEE Trans. Pattern Anal. Mach. Intell.

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Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples for these models. These adversarial samples can become a security concern. In this paper, we propose utilizing an adversarial transformation network (ATN) on a distilled model to attack various time series classification models. The proposed attack on the classification model utilizes a distilled model as a surrogate that mimics the behavior of the attacked classical time series classification models. Our proposed methodology is applied onto 1-Nearest Neighbor Dynamic Time Warping (1-NN ) DTW, a Fully Connected Network and a Fully Convolutional Network (FCN), all of which are trained on 42 University of California Riverside (UCR) datasets. In this paper, we show both models were susceptible to attacks on all 42 datasets. To the best of our knowledge, such an attack on time series classification models has never been done before. Finally, we recommend future researchers that develop time series classification models to incorporating adversarial data samples into their training data sets to improve resilience on adversarial samples and to consider model robustness as an evaluative metric.

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Section: A Experimentsmentioning

confidence: 99%

Adversarial Attacks on Time Series

Karim

Majumdar

Darabi

2021

IEEE Trans. Pattern Anal. Mach. Intell.

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“…This algorithm exactly computes the dtw-distance for time series consisting of binary values and is an approximation of the dtw-distance otherwise. The second algorithm is the bdtw-distance (block-dtw) proposed by [34]. The bdtw-algorithm generalizes AWarp by exactly computing the dtw-distance not only for binary but also for any two-valued time series.…”

Section: Computational Issuesmentioning

confidence: 99%

Making the dynamic time warping distance warping-invariant

Jain

2019

Pattern Recognition

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The literature postulates that the dynamic time warping (dtw) distance can cope with temporal variations but stores and processes time series in a form as if the dtw-distance cannot cope with such variations. To address this inconsistency, we first show that the dtw-distance is not warping-invariant. The lack of warping-invariance contributes to the inconsistency mentioned above and to a strange behavior. To eliminate these peculiarities, we convert the dtw-distance to a warping-invariant semi-metric, called time-warp-invariant (twi) distance. Empirical results suggest that the error rates of the twi and dtw nearest-neighbor classifier are practically equivalent in a Bayesian sense. However, the twi-distance requires less storage and computation time than the dtw-distance for a broad range of problems. These results challenge the current practice of applying the dtw-distance in nearest-neighbor classification and suggest the proposed twi-distance as a more efficient and consistent option.

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“…These methods are relatively simple to calculate, but the recognition rate is relatively low. Shi and Luo [29] introduced the concept of ''entropy'' and proposed a human motion model based on motion energy, using dynamic time warping [30] algorithm to realize action recognition.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection and Motion Recognition of Human Moving Objects Based on Deep Learning and Multi-Scale Feature Fusion in Video

Mei-mei

Shu

2020

IEEE Access

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At present, human body moving target detection and recognition algorithms based on deep learning have made breakthrough progress. However, in some applications with high real-time requirements, the existing deep learning real-time detection and recognition network is difficult to achieve high detection accuracy. Therefore, how to achieve accurate positioning and recognition of human moving targets while ensuring real-time detection is still an urgent problem in this field. Based on the single shot multi-box detector (SSD) real-time detection network, this paper proposes a real-time detection positioning and recognition network based on multi-scale feature fusion (IMFF-SSD), which improves the positioning accuracy and identification accuracy. First, this article analyzes the multi-scale features extracted from the SSD network. It combines the position-sensitive information provided by low-level detail features with the context information provided by high-level semantic features through feature fusion, which effectively improves positioning accuracy of the target prediction layer in the SSD network. Secondly, a feature embedded prediction structure is designed to strengthen the semantics of target features without changing the spatial resolution of the SSD prediction layer, and embed low-scale detailed features in high-semantic features for collaborative prediction of targets. This improves the accuracy of the SSD network's recognition of human moving targets at all scales. The experimental results show that by combining the above two improvements, the real-time monitoring and recognition network based on multi-scale feature fusion proposed in this paper has achieved a greater degree of improvement in positioning accuracy and motion recognition accuracy than the original SSD, which is better than some current the human body moving object detection and recognition algorithm has great advantages. INDEX TERMS Deep learning, real-time, detection and motion recognition, multi-scale feature fusion.

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Asymptotic Dynamic Time Warping calculation with utilizing value repetition

Cited by 21 publications

References 24 publications

Adversarial Attacks on Time Series

Adversarial Attacks on Time Series

Making the dynamic time warping distance warping-invariant

Real-Time Detection and Motion Recognition of Human Moving Objects Based on Deep Learning and Multi-Scale Feature Fusion in Video

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