A review of vision-based traffic semantic understanding in ITSs
J Chen, Q Wang, HH Cheng, W Peng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
A semantic understanding of road traffic can help people understand road traffic flow
situations and emergencies more accurately and provide a more accurate basis for anomaly …
situations and emergencies more accurately and provide a more accurate basis for anomaly …
Anomaly detection in traffic surveillance videos using deep learning
In the recent past, a huge number of cameras have been placed in a variety of public and
private areas for the purposes of surveillance, the monitoring of abnormal human actions …
private areas for the purposes of surveillance, the monitoring of abnormal human actions …
Spatio-temporal feature encoding for traffic accident detection in VANET environment
In the Vehicular Ad hoc Networks (VANET) environment, recognizing traffic accident events
in the driving videos captured by vehicle-mounted cameras is an essential task. Generally …
in the driving videos captured by vehicle-mounted cameras is an essential task. Generally …
Long short-term transformer for online action detection
Abstract We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm
for online action detection, which employs a long-and short-term memory mechanism to …
for online action detection, which employs a long-and short-term memory mechanism to …
Stepwise goal-driven networks for trajectory prediction
We propose to predict the future trajectories of observed agents (eg, pedestrians or vehicles)
by estimating and using their goals at multiple time scales. We argue that the goal of a …
by estimating and using their goals at multiple time scales. We argue that the goal of a …
Bitrap: Bi-directional pedestrian trajectory prediction with multi-modal goal estimation
Pedestrian trajectory prediction is an essential task in robotic applications such as
autonomous driving and robot navigation. State-of-the-art trajectory predictors use a …
autonomous driving and robot navigation. State-of-the-art trajectory predictors use a …
Localizing anomalies from weakly-labeled videos
Video anomaly detection under video-level labels is currently a challenging task. Previous
works have made progresses on discriminating whether a video sequence contains …
works have made progresses on discriminating whether a video sequence contains …
Uncertainty-based traffic accident anticipation with spatio-temporal relational learning
Traffic accident anticipation aims to predict accidents from dashcam videos as early as
possible, which is critical to safety-guaranteed self-driving systems. With cluttered traffic …
possible, which is critical to safety-guaranteed self-driving systems. With cluttered traffic …
Temporal recurrent networks for online action detection
Most work on temporal action detection is formulated as an offline problem, in which the start
and end times of actions are determined after the entire video is fully observed. However …
and end times of actions are determined after the entire video is fully observed. However …
[HTML][HTML] Car crash detection using ensemble deep learning and multimodal data from dashboard cameras
Due to the increase in motor vehicle accidents, there is a growing need for high-
performance car crash detection systems. The authors of this research propose a car crash …
performance car crash detection systems. The authors of this research propose a car crash …