35,960 research outputs found
Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition
Recently, Long Short-Term Memory (LSTM) has become a popular choice to model
individual dynamics for single-person action recognition due to its ability of
modeling the temporal information in various ranges of dynamic contexts.
However, existing RNN models only focus on capturing the temporal dynamics of
the person-person interactions by naively combining the activity dynamics of
individuals or modeling them as a whole. This neglects the inter-related
dynamics of how person-person interactions change over time. To this end, we
propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to
model the long-term inter-related dynamics between two interacting people on
the bounding boxes covering people. Specifically, for each frame, two
sub-memory units store individual motion information, while a concurrent LSTM
unit selectively integrates and stores inter-related motion information between
interacting people from these two sub-memory units via a new co-memory cell.
Experimental results on the BIT and UT datasets show the superiority of
Co-LSTSM compared with the state-of-the-art methods
Stochastic Prediction of Multi-Agent Interactions from Partial Observations
We present a method that learns to integrate temporal information, from a
learned dynamics model, with ambiguous visual information, from a learned
vision model, in the context of interacting agents. Our method is based on a
graph-structured variational recurrent neural network (Graph-VRNN), which is
trained end-to-end to infer the current state of the (partially observed)
world, as well as to forecast future states. We show that our method
outperforms various baselines on two sports datasets, one based on real
basketball trajectories, and one generated by a soccer game engine.Comment: ICLR 2019 camera read
Tracking in Urban Traffic Scenes from Background Subtraction and Object Detection
In this paper, we propose to combine detections from background subtraction
and from a multiclass object detector for multiple object tracking (MOT) in
urban traffic scenes. These objects are associated across frames using spatial,
colour and class label information, and trajectory prediction is evaluated to
yield the final MOT outputs. The proposed method was tested on the Urban
tracker dataset and shows competitive performances compared to state-of-the-art
approaches. Results show that the integration of different detection inputs
remains a challenging task that greatly affects the MOT performance
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Current multi-person localisation and tracking systems have an over reliance
on the use of appearance models for target re-identification and almost no
approaches employ a complete deep learning solution for both objectives. We
present a novel, complete deep learning framework for multi-person localisation
and tracking. In this context we first introduce a light weight sequential
Generative Adversarial Network architecture for person localisation, which
overcomes issues related to occlusions and noisy detections, typically found in
a multi person environment. In the proposed tracking framework we build upon
recent advances in pedestrian trajectory prediction approaches and propose a
novel data association scheme based on predicted trajectories. This removes the
need for computationally expensive person re-identification systems based on
appearance features and generates human like trajectories with minimal
fragmentation. The proposed method is evaluated on multiple public benchmarks
including both static and dynamic cameras and is capable of generating
outstanding performance, especially among other recently proposed deep neural
network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer
Vision (WACV), 201
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