Machine Learning Methods for Radar-Based People Detection and Tracking

Castanheira, José; Curado, Francisco; Tomé, Ana Maria; Gonçalves, Edgar S.

doi:10.1007/978-3-030-30241-2_35

Cited by 6 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Major applications have included the detection and tracking of other road users, e.g. vehicles [30], pedestrians [31], and cyclists [32], human behaviour classification [33], and the detection of lane and road markings Fig. 1: Traffic scene showing vehicles and road network.…”

Section: B Automotive Radar Applicationsmentioning

confidence: 99%

Predicting Vehicle Behavior Using Automotive Radar and Recurrent Neural Networks

Mukherjee

Wallace

Wang

2021

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

We present a Long Short Term Memory (LSTM) encoder-decoder architecture to anticipate the future positions of vehicles in a road network given several seconds of historical observations and associated map features. Unlike existing architectures, the proposed method incorporates and updates the surrounding vehicle information in both the encoder and decoder, making use of dynamically predicted new data for accurate prediction in longer time horizons. It seamlessly performs four tasks: the first task encodes a feature given the past observations, the second task estimates future maneuvers given the encoded state, the third task predicts the future motion given the estimated maneuvers and the initially encoded states, and the fourth task estimates future trajectory given the encoded state and the predicted maneuvers and motions. Experiments on a public benchmark and a new, publicly available radar dataset demonstrate that our approach can equal or surpass the state-of-the-art for long term trajectory prediction.

show abstract

Section: B Automotive Radar Applicationsmentioning

confidence: 99%

Predicting Vehicle Behavior Using Automotive Radar and Recurrent Neural Networks

Mukherjee

Wallace

Wang

2021

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

show abstract

“…The potential of radar-based sensing and recognition technologies has been discovered across a variety of different scientific domains, and they have been the target of numerous previous and recent research studies. The first studies dealt with the detection and recognition of humans in indoor environments in applications related to security [1][2][3][4][5][6]. Medical applications, i.e., the monitoring of patients, extended their applicability [7][8][9][10][11][12][13] to sub-domains, e.g., vital sign detection.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Recent Progress of Machine Learning-Based Human Activity Recognition with Radar

Papadopoulos,

Jelali

2023

Applied Sciences

View full text Add to dashboard Cite

The importance of radar-based human activity recognition has increased significantly over the last two decades in safety and smart surveillance applications due to its superiority in vision-based sensing in the presence of poor environmental conditions like low illumination, increased radiative heat, occlusion, and fog. Increased public sensitivity to privacy protection and the progress of cost-effective manufacturing have led to higher acceptance and distribution of this technology. Deep learning approaches have proven that manual feature extraction that relies heavily on process knowledge can be avoided due to its hierarchical, non-descriptive nature. On the other hand, ML techniques based on manual feature extraction provide a robust, yet empirical-based approach, where the computational effort is comparatively low. This review outlines the basics of classical ML- and DL-based human activity recognition and its advances, taking the recent progress in both categories into account. For every category, state-of-the-art methods are introduced, briefly explained, and their related works summarized. A comparative study is performed to evaluate the performance and computational effort based on a benchmarking dataset to provide a common basis for the assessment of the techniques’ degrees of suitability.

show abstract

Social Distancing Model Utilizing Machine Learning Techniques

Mohamed

Samee

2021

Studies in Systems, Decision and Control

View full text Add to dashboard Cite

Machine Learning Methods for Radar-Based People Detection and Tracking

Cited by 6 publications

References 4 publications

Predicting Vehicle Behavior Using Automotive Radar and Recurrent Neural Networks

Predicting Vehicle Behavior Using Automotive Radar and Recurrent Neural Networks

A Comparative Study on Recent Progress of Machine Learning-Based Human Activity Recognition with Radar

Social Distancing Model Utilizing Machine Learning Techniques

Contact Info

Product

Resources

About