4,465 research outputs found
CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction
Pedestrian path prediction is an essential topic in computer vision and video
understanding. Having insight into the movement of pedestrians is crucial for
ensuring safe operation in a variety of applications including autonomous
vehicles, social robots, and environmental monitoring. Current works in this
area utilize complex generative or recurrent methods to capture many possible
futures. However, despite the inherent real-time nature of predicting future
paths, little work has been done to explore accurate and computationally
efficient approaches for this task. To this end, we propose a convolutional
approach for real-time pedestrian path prediction, CARPe. It utilizes a
variation of Graph Isomorphism Networks in combination with an agile
convolutional neural network design to form a fast and accurate path prediction
approach. Notable results in both inference speed and prediction accuracy are
achieved, improving FPS considerably in comparison to current state-of-the-art
methods while delivering competitive accuracy on well-known path prediction
datasets.Comment: AAAI-21 Camera Read
Application of the self-organising map to trajectory classification
This paper presents an approach to the problem of automatically classifying events detected by video surveillance systems; specifically, of detecting unusual or suspicious movements. Approaches to this problem typically involve building complex 3D-models in real-world coordinates
to provide trajectory information for the classifier. In this paper we show that analysis of trajectories may be carried out in a model-free fashion, using self-organising
feature map neural networks to learn the characteristics of normal trajectories, and to detect novel ones. Trajectories are represented using positional and first and second order motion information, with moving-average smoothing. This allows novelty detection to be applied on a point-by-point basis in real time, and permits both instantaneous motion and whole trajectory motion to be subjected to novelty detection
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
We present a novel procedural framework to generate an arbitrary number of
labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to
design accurate algorithms or training models for crowded scene understanding.
Our overall approach is composed of two components: a procedural simulation
framework for generating crowd movements and behaviors, and a procedural
rendering framework to generate different videos or images. Each video or image
is automatically labeled based on the environment, number of pedestrians,
density, behavior, flow, lighting conditions, viewpoint, noise, etc.
Furthermore, we can increase the realism by combining synthetically-generated
behaviors with real-world background videos. We demonstrate the benefits of
LCrowdV over prior lableled crowd datasets by improving the accuracy of
pedestrian detection and crowd behavior classification algorithms. LCrowdV
would be released on the WWW
Transformer Networks for Trajectory Forecasting
Most recent successes on forecasting the people motion are based on LSTM
models and all most recent progress has been achieved by modelling the social
interaction among people and the people interaction with the scene. We question
the use of the LSTM models and propose the novel use of Transformer Networks
for trajectory forecasting. This is a fundamental switch from the sequential
step-by-step processing of LSTMs to the only-attention-based memory mechanisms
of Transformers. In particular, we consider both the original Transformer
Network (TF) and the larger Bidirectional Transformer (BERT), state-of-the-art
on all natural language processing tasks. Our proposed Transformers predict the
trajectories of the individual people in the scene. These are "simple" model
because each person is modelled separately without any complex human-human nor
scene interaction terms. In particular, the TF model without bells and whistles
yields the best score on the largest and most challenging trajectory
forecasting benchmark of TrajNet. Additionally, its extension which predicts
multiple plausible future trajectories performs on par with more engineered
techniques on the 5 datasets of ETH + UCY. Finally, we show that Transformers
may deal with missing observations, as it may be the case with real sensor
data. Code is available at https://github.com/FGiuliari/Trajectory-Transformer.Comment: 18 pages, 3 figure
- …