Movement Pattern Analysis Based on Sequence Signatures

Chavoshi, Seyed Hossein; Baets, Bernard De; Neutens, Tijs; Delafontaine, Matthias; Tré, Guy De; Weghe, Nico Van de

doi:10.3390/ijgi4031605

Cited by 3 publications

(3 citation statements)

References 58 publications

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“…Their technique initially determines the simplified dense trajectories of a single object and then converts these trajectories into 2D images. Chavoshi et al [5] presented a visualization technique, sequence signature (SESI), to convert a basic variation of QTC (QT C B ) movement patterns of moving point objects into a 2D indexed rasterized matrix. The approach in [12] represents trajectories of pair-vehicles as a series of heat sources, where a thermal diffusion process creates an activity map as a 2D matrix.…”

Section: Trajectory Analysis Techniquesmentioning

confidence: 99%

Vehicle Activity Recognition Using DCNN

AlZoubi

Nam

2020

Communications in Computer and Information Science

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This paper presents a novel Deep Convolutional Neural Network (DCNN) method for vehicle activity classification. We extend our previous approach to be able to classify a larger number of vehicle trajectories in a single network. We also highlight the flexibility of our approach in integrating further scenarios to our classifier. Firstly, a spatiotemporal calculus method is used to encode the relative movement between vehicles as a trajectory of QTC states. We then map the encoded trajectory to a 2D matrix using the one-hot vector mapping, this preserves the important positional data and order for each QTC state. To do this we associate the QTC sequences with pixels to form a 2D image texture. Afterwards, we adapted trained CNN architecture into our vehicles activity recognition task. Two separate types of driving data sets are used to evaluate our method. We demonstrate that the proposed method out-performs existing techniques. Along with the proposed approach we created a new dataset of vehicles interactions. Although the focus of this paper is on the automated analysis of vehicle interactions, the proposed technique is general and can be applied for pairwise analysis for moving objects.

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Section: Trajectory Analysis Techniquesmentioning

confidence: 99%

Vehicle Activity Recognition Using DCNN

AlZoubi

Nam

2020

Communications in Computer and Information Science

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“…The method first extracts the simplified dense trajectories of single object and then converts these trajectories into 2D images. Chavoshi et al (Chavoshi et al, 2015) proposed a visualization technique, sequence signature (SESI), to transform the simplest variant of QTC (QTC B ) movement patterns of moving point objects (MPOs) into a 2D indexed rasterized space. The method in (Lin et al, 2013) represents trajectories of pair-vehicles as a series of heat sources; then, a thermal diffusion process creates an activity map (or 2D matrix).…”

Section: Related Workmentioning

confidence: 99%

Vehicle Activity Recognition using Mapped QTC Trajectories

AlZoubi

Nam

2019

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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The automated analysis of interacting objects or vehicles has many uses, including autonomous driving and security surveillance. In this paper we present a novel method for vehicle activity recognition using Deep Convolutional Neural Network (DCNN). We use Qualitative Trajectory Calculus (QTC) to represent the relative motion between pair of vehicles, and encode their interactions as a trajectory of QTC states. We then use one-hot vectors to map the trajectory into 2D matrix which conserves the essential position information of each QTC state in the sequence. Specifically, we project QTC sequences into a two dimensional image texture, and subsequently our method adapt layers trained on the ImageNet dataset and transfer this knowledge to the activity recognition task. We have evaluated our method using two different datasets, and shown that it out-performs state-of-the-art methods, achieving an error rate of no more than 1.16%. Our motivation originates from an interest in automated analysis of vehicle movement for the collision avoidance application, and we present a dataset of vehicle-obstacle interaction, collected from simulator-based experiments.

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“…It could be advantageous to facilitate the application of MPA to explore methods for integrating automated methods that might be implemented without eliminating the essential expert perspective; such efforts could, for example, permit either more rapid coding of an MPA interview along with facilitating broader application to larger numbers of individuals. While we posit that the complexity of detecting PGMs—highly integrated movement patterns that require perception of the whole body in motion—will prove to be a substantial challenge for developers of automated coding systems, there are studies which have shown initial levels of success at representing the body movements of a samba dancer (Chavoshi et al, 2015 ), along with application of LMA to detect hand movements (Lourens et al, 2010 ) and segmentation of a repertoire of motions (Bouchard and Badler, 2007 ).…”

Section: Future Directionsmentioning

confidence: 99%

Embodied Decision-Making Style: Below and Beyond Cognition

Connors

Rende

2018

Front. Psychol.

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There is growing recognition of the essential role of sensorimotor processes as not just a supporter of the cognitive aspects of decision making, but rather as a foundation for all the coordinated physical and mental activities that go into how we make decisions. We illuminate concepts and methods for examining embodied decision making through the lens of Movement Pattern Analysis (MPA). MPA is as a prime example of a conceptually rooted observational methodology for deciphering embodied decision making and for decoding how people differ as decision makers with respect to cognitive motivational priorities. The historical origins of MPA that predated the formalized recognition of embodied cognition are presented, along with an overview of both the theoretical model and methodology. Advances in research on two psychometric benchmarks of observational research—inter-rater reliability and predictive validity—are highlighted as an empirical platform for the strong promise of MPA as a tool for understanding individual differences in embodied decision-making style. Future directions for research are considered—specifically with respect to the potential for utilizing automated coding, and the need for collaborative neuroscience research efforts—which would support further understanding of how decoding movement patterning captures human motivation at the level of sensory, motoric, cognitive and action integration which drives how people function as decision makers.

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Movement Pattern Analysis Based on Sequence Signatures

Cited by 3 publications

References 58 publications

Vehicle Activity Recognition Using DCNN

Vehicle Activity Recognition Using DCNN

Vehicle Activity Recognition using Mapped QTC Trajectories

Embodied Decision-Making Style: Below and Beyond Cognition

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