A Single-Shot Approach Using an LSTM for Moving Object Path Prediction
Publication Type:
Edited Conference Meeting Proceeding
Abstract:
This work presents an analysis of predicting the
future path of moving objects from a moving camera on traffic
scenes with an LSTM architecture in a single-shot manner. Path
prediction allows us to estimate the future locations of an object
in a given space and is useful in important applications such
as surveillance, abnormal behaviour detection, crowd behaviour
analysis, traffic control and currently in driver assistance (ADAS)
or collision avoidance systems. Normal approaches use the
last tobs positions of an object observed in video frames to
predict its future path as a sequence of position values. This
can then be treated as a time series. LSTM architectures are
known for reaching good performance when dealing with time
series. We evaluate path prediction across three types of objects
(pedestrians, vehicles and cyclists), four prediction horizons (5,
10, 15 and 20 frames ahead) and two different perspectives (image
coordinate and birds-eye view). The approach described in this
work reached an Average Displacement Error (ADE) of 0.01m
for pedestrians, 0.06m for vehicles and 0.02m for cyclists and
an average Final Displacement Error (FDE) of between 0.016m
and 0.15m for near-future prediction using an LSTM architecure
with relative tracklet positioning.
Conference Name:
2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)
Proceedings:
2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)
Digital Object Identifer (DOI):
10.1109/IPTA.2019.8936126
Publication Date:
19/12/2019
Conference Location:
Turkey
Research Group:
Institution:
Dublin City University (DCU)
Open access repository:
Yes
Publication document: