Authors :
Raghav Utpat; Sameer Joshi; Seema Chandak; Shaunak Joshi; Shubham Loya
Volume/Issue :
Volume 6 - 2021, Issue 4 - April
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/3uZ0cmb
Abstract :
Navigation in cluttered and crowded
environments has been an important and difficult
problem in technology. This involves accurately
predicting pedestrians’ movements, dynamically
analysing developments in the surroundings, and
adjusting the path accordingly. This paper focuses on
solving the navigation problem by predicting the
trajectories of pedestrians. Humans are identified and
tracked using state-of-the-art object detection
techniques. R-CNN and YOLO are proven to have the
best accuracy and speed to perform the task. We used
both social and non-social algorithms to predict
trajectories of the detected pedestrians. These
trajectories are used to estimate future positions of the
pedestrians. Finally these positions are used to calculate
the path through the environment.
Keywords :
Navigation, Realtime, Trajectory Prediction, Deep Learning, Object Detection, Path planning, Long Short Term Memory Network (LSTM), Region based Convolutional Network (R-CNN)
Navigation in cluttered and crowded
environments has been an important and difficult
problem in technology. This involves accurately
predicting pedestrians’ movements, dynamically
analysing developments in the surroundings, and
adjusting the path accordingly. This paper focuses on
solving the navigation problem by predicting the
trajectories of pedestrians. Humans are identified and
tracked using state-of-the-art object detection
techniques. R-CNN and YOLO are proven to have the
best accuracy and speed to perform the task. We used
both social and non-social algorithms to predict
trajectories of the detected pedestrians. These
trajectories are used to estimate future positions of the
pedestrians. Finally these positions are used to calculate
the path through the environment.
Keywords :
Navigation, Realtime, Trajectory Prediction, Deep Learning, Object Detection, Path planning, Long Short Term Memory Network (LSTM), Region based Convolutional Network (R-CNN)
