Evaluating the effect of compressing algorithms for trajectory similarity and classification problems

Makris, Antonios; Silva, Camila Leite da; Bogorny, Vânia; Älvares, Luís Otávio; Macêdo, José Antônio Fernandes de; Tserpes, Konstantinos

doi:10.1007/s10707-021-00434-1

Cited by 17 publications

(6 citation statements)

References 42 publications

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“…Many of these datasets have already being used in [6] and [44] for comparing several classification methods. A detailed description of the trajectory datasets is presented below.…”

Section: Datasetmentioning

confidence: 99%

TraClets: Harnessing the power of computer vision for trajectory classification

Kontopoulos¹,

Makris²,

Tserpes³

et al. 2022

Preprint

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Due to the advent of new mobile devices and tracking sensors in recent years, huge amounts of data are being produced every day. Therefore, novel methodologies need to emerge that dive through this vast sea of information and generate insights and meaningful information. To this end, researchers have developed several trajectory classification algorithms over the years that are able to annotate tracking data. Similarly, in this research, a novel methodology is presented that exploits image representations of trajectories, called TraClets, in order to classify trajectories in an intuitive humans way, through computer vision techniques. Several real-world datasets are used to evaluate the proposed approach and compare its classification performance to other state-of-the-art trajectory classification algorithms. Experimental results demonstrate that TraClets achieves a classification performance that is comparable to, or in most cases, better than the state-of-the-art, acting as a universal, high-accuracy approach for trajectory classification.

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“…Many of these datasets have already being used in [6] and [44] for comparing several classification methods. A detailed description of the trajectory datasets is presented below.…”

Section: Datasetmentioning

confidence: 99%

TraClets: Harnessing the power of computer vision for trajectory classification

Kontopoulos¹,

Makris²,

Tserpes³

et al. 2022

Preprint

Self Cite

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“…The turning point is obviously very important for determining trajectory ( Makris et al, 2021b ; Sousa, Boukerche & Loureiro, 2021 ; Zhan et al, 2014 ). Because trajectory data compression is based on the premise that trajectory shape does not change, turning points should be preserved during compression ( Chen & Chen, 2021 ; Zhou, Qu & Toivonen, 2017 ; Fu & Lee, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A trajectory data compression algorithm based on spatio-temporal characteristics

Zhong

Kong

Zhang

et al. 2022

PeerJ Computer Science

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Background With the growth of trajectory data, the large amount of data causes a lot of problems with storage, analysis, mining, etc. Most of the traditional trajectory data compression methods are focused on preserving spatial characteristic information and pay little attention to other temporal information on trajectory data, such as speed change points or stop points. Methods A data compression algorithm based on the spatio-temporal characteristics (CASC) of the trajectory data is proposed to solve this problem. This algorithm compresses trajectory data by taking the azimuth difference, velocity difference and time interval as parameters in order to preserve spatial-temporal characteristics. Microsoft’s Geolife1.3 data set was used for a compression test to verify the validity of the algorithm. The compression results were compared with the traditional Douglas-Peucker (DP), Top-Down Time Ratio (TD-TR) and Opening Window (OPW) algorithms. Compression rate, the direction information of trajectory points, vertical synchronization distance, and algorithm type (online/offline) were used to evaluate the above algorithms. Results The experimental results show that with the same compression rate, the ability of the CASC to retain the forward direction trajectory is optimal, followed by TD-TR, DP, and then OPW. The velocity characteristics of the trajectories are also stably retained when the speed threshold value is not more than 100%. Unlike the DP and TD-TR algorithms, CASC is an online algorithm. Compared with OPW, which is also an online algorithm, CASC has better compression quality. The error distributions of the four algorithms have been compared, and CASC is the most stable algorithm. Taken together, CASC outperforms DP, TD-TR and OPW in trajectory compression.

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“…The existing methods of trajectory data compression can be divided into three categories [8][9][10][11]. The first is a trajectory data compression based on line segment simplification [12][13][14][15][16][17]; the trajectory in free space is segmented linearly by constraints, and only two end points of each approximate segment are stored to achieve compression.…”

Section: Introductionmentioning

confidence: 99%

PSOTSC: A Global-Oriented Trajectory Segmentation and Compression Algorithm Based on Swarm Intelligence

Ouyang

Ding

et al. 2021

IJGI

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Linear approximate segmentation and data compression of moving target spatio-temporal trajectory can reduce data storage pressure and improve the efficiency of target motion pattern mining. High quality segmentation and compression need to accurately select and store as few points as possible that can reflect the characteristics of the original trajectory, while the existing methods still have room for improvement in segmentation accuracy, reduction of compression rate and simplification of algorithm parameter setting. A trajectory segmentation and compression algorithm based on particle swarm optimization is proposed. First, the trajectory segmentation problem is transformed into a global intelligent optimization problem of segmented feature points, which makes the selection of segmented points more accurate; then, a particle update strategy combining neighborhood adjustment and random jump is established to improve the efficiency of segmentation and compression. Through experiments on a real data set and a maneuvering target simulation trajectory set, the results show that compared with the existing typical methods, this method has advantages in segmentation accuracy and compression rate.

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Evaluating the effect of compressing algorithms for trajectory similarity and classification problems

Cited by 17 publications

References 42 publications

TraClets: Harnessing the power of computer vision for trajectory classification

TraClets: Harnessing the power of computer vision for trajectory classification

A trajectory data compression algorithm based on spatio-temporal characteristics

PSOTSC: A Global-Oriented Trajectory Segmentation and Compression Algorithm Based on Swarm Intelligence

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