Implementation Of Dynamic Time Warping Research Articles

Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2

The increasing volume of remote sensing data with improved spatial and temporal resolutions generates unique opportunities for monitoring and mapping of crops. We compared multiple single-band and multi-band object-based time-constrained Dynamic Time Warping (DTW) classifications for crop mapping based on Sentinel-2 time series of vegetation indices. We tested it on two complex and intensively managed agricultural areas in California and Texas. DTW is a time-flexible method for comparing two temporal patterns by considering their temporal distortions in their alignment. For crop mapping, using time constraints in computing DTW is recommended in order to consider the seasonality of crops. We tested different time constraints in DTW (15, 30, 45, and 60 days) and compared the results with those obtained by using Euclidean distance or a DTW without time constraint. Best classification results were for time delays of both 30 and 45 days in California: 79.5% for single-band DTWs and 85.6% for multi-band DTWs. In Texas, 45 days was best for single-band DTW (89.1%), while 30 days yielded best results for multi-band DTW (87.6%). Using temporal information from five vegetation indices instead of one increased the overall accuracy in California with 6.1%. We discuss the implications of DTW dissimilarity values in understanding the classification errors. Considering the possible sources of errors and their propagation throughout our analysis, we had combined errors of 22.2% and 16.8% for California and 24.6% and 25.4% for Texas study areas. The proposed workflow is the first implementation of DTW in an object-based image analysis (OBIA) environment and represents a promising step towards generating fast, accurate, and ready-to-use agricultural data products.

Efficient Classification of Long Time Series by 3-D Dynamic Time Warping

Throughout recent years, dynamic time warping (DTW) has remained as a robust similarity measure in time series classification (TSC). 1-nearest neighbor (1-NN) algorithm with DTW is the most widely used classification method on time series serving as a benchmark. With the increasing demand for TSC on low-resource devices and the widespread of wearable devices, the need for a efficient and accurate time series classifier has never been higher. Although 1-NN DTW attains accurate results, it highly falls back on efficiency due to its quadratic complexity in the length of time series. In this paper, we propose a new approximation method for reducing the length of the time series as the input of DTW. We call it control chart approximation (CCA), after a similar concept used in statistical quality control processing. CCA representation approximates raw time series by transforming them into a set of segments with aggregated values and durations forming a reduced 3-D vector. We also propose an adaptation of DTW in 3-D space as a distance measure for 1-NN classifier, and denote the method as 1-NN 3-D DTW. Our experiments on 85 datasets from UCR archive—including 28 long-length (>500 points) time series datasets—show up to two orders of magnitude performance gain in running time compared to the state-of-the-art 1-NN DTW implementation. Moreover, it shows similar or better accuracy on the long time series in the experiment.

FIELD-PROGRAMMABLE GATE ARRAY IMPLEMENTATION OF THE DYNAMIC TIME WARPING ALGORITHM FOR SPEECH RECOGNITION

Objective of this research is to implement a speech recognition algorithm in smaller form factor device. Speech recognition is an extensively used inmobile and in numerous consumer electronics devices. Dynamic time warping (DTW) method which is based on dynamic programming is chosen tobe implemented for speech recognition because of the latest trend in evolving computing power. Implementation of DTW in field-programmable gatearray is chosen for its featured flexibility, parallelization and shorter time to market. The above algorithm is implemented using Verilog on Xilinx ISE.The warping cost is less if the similarity is found and is more for dissimilar sequences which is verified in the simulation output. The results indicatethat real time implementation of DTW based speech recognition could be done in future.

Hand Gesture User Interface for Smart Devices Based on Mems Sensors

This paper presents a MEMS accelerometer based handheld embedded device and its associated dynamic time warping (DTW) based algorithm for recognizing hand writing gestures. Device can be used to draw gestures for English lower case letters, Arabic numerals in their own style and speed. Furthermore the proposed device can detect different direction movement such as left, right, up, down, push and pull for employing video game and mobile game control. The accelerometer signals are processed within the device itself and result of recognition is transmitted via Bluetooth to smart device such as computer, smart phone, tablets etc. Furthermore we have introduced a novel method of fast and memory efficient implementation of DTW in embedded domain. Also we have developed an effective motion detection method based on adaptive mean value calculation of accelerometer signals also we introduced an effective template selection criterion. The effectiveness of proposed system and associated algorithm were validated through various experiments.

Exploring the Benefits of Context in 3D Gesture Recognition for Game-Based Virtual Environments

We present a systematic exploration of how to utilize video game context (e.g., player and environmental state) to modify and augment existing 3D gesture recognizers to improve accuracy for large gesture sets. Specifically, our work develops and evaluates three strategies for incorporating context into 3D gesture recognizers. These strategies include modifying the well-known Rubine linear classifier to handle unsegmented input streams and per-frame retraining using contextual information (CA-Linear); a GPU implementation of dynamic time warping (DTW) that reduces the overhead of traditional DTW by utilizing context to evaluate only relevant time sequences inside of a multithreaded kernel (CA-DTW); and a multiclass SVM with per-class probability estimation that is combined with a contextually based prior probability distribution (CA-SVM). We evaluate each strategy using a Kinect-based third-person perspective VE game prototype that combines parkour-style navigation with hand-to-hand combat. Using a simple gesture collection application to collect a set of 57 gestures and the game prototype that implements 37 of these gestures, we conduct three experiments. In the first experiment, we evaluate the effectiveness of several established classifiers on our gesture set and demonstrate state-of-the-art results using our proposed method. In our second experiment, we generate 500 random scenarios having between 5 and 19 of the 57 gestures in context. We show that the contextually aware classifiers CA-Linear, CA-DTW, and CA-SVM significantly outperform their non--contextually aware counterparts by 37.74%, 36.04%, and 20.81%, respectively. On the basis of the results of the second experiment, we derive upper-bound expectations for in-game performance for the three CA classifiers: 96.61%, 86.79%, and 96.86%, respectively. Finally, our third experiment is an in-game evaluation of the three CA classifiers with and without context. Our results show that through the use of context, we are able to achieve an average in-game recognition accuracy of 89.67% with CA-Linear compared to 65.10% without context, 79.04% for CA-DTW compared to 58.1% without context, and 90.85% with CA-SVM compared to 75.2% without context.

A robust video stabilization technique using integral frame projection warping

In this paper, a video stabilization technique based on optimized dynamic time warping (DTW) has been proposed. The method uses integral frame projection warping (IFPW) for the relative motion estimation between consecutive frames. Implementation of DTW is done using dynamic programming, to give a significant reduction in terms of memory and processing time. Most of the motion estimation techniques are adversely affected due to blurring, low intensity and large displacements. The proposed method gives accurate results under these conditions and proves to be a robust for various applications such as moving platform and night shooting, over existing intensity-based motion estimation techniques. Efficiency of the proposed IFPW technique, for dark video and blurry frames, is measured using motion estimation error analysis. Overall performance evaluation of the stabilization system is done using interframe transformation fidelity and processing time.

A vector-quantization-based preprocessor for speaker-independent isolated word recognition

In this paper, we propose a speaker-independent isolated ward recognition system whose performance is comparable to that of a conventional isolated word recognizer, but whose computation is greatly reduced. The structure of the proposed recognizer consists of a word-based vector quantization (VQ) preprocessor, followed by a conventional DTW postprocessor. The purpose of the preprocessor is essentially to eliminate from further consideration all words in the vocabulary which are unlikely recognition candidates. In some cases, the preprocessor will be able to eliminate all word candidates except one; for such cases, there is no further processing required for word recognition. In all other cases (i.e., when more than one word candidate is passed on), a dynamic time warping (DTW) processor is used to re-solve finer acoustical distinctions among the remaining word candidates. The performance of this type of recognizer (i.e., using a word-based preprocessor and a standard DTW comparison to make finer distinctions) is affected by a number of factors involved with the details of exactly how the system is implemented-e.g., the distortion measure used in the preprocessor and in the DTW comparison, the size of the VQ codebook for each vocabulary word, the decision thresholds of the preprocessor, etc. Several of these factors were studied experimentally using testing databases consisting of isolated digits and words from a vocabulary of 129 airline terms. The results show that the proposed preprocessor has the capability of reducing computation for recognition by up to an order of magnitude, while maintaining the same performance as that obtained using a DTW comparison without the pre-processor. A somewhat smaller reduction in memory over the straight DTW implementation is also obtained in the proposed approach.