3,734 research outputs found
Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification
Online multi-object tracking is a fundamental problem in time-critical video
analysis applications. A major challenge in the popular tracking-by-detection
framework is how to associate unreliable detection results with existing
tracks. In this paper, we propose to handle unreliable detection by collecting
candidates from outputs of both detection and tracking. The intuition behind
generating redundant candidates is that detection and tracks can complement
each other in different scenarios. Detection results of high confidence prevent
tracking drifts in the long term, and predictions of tracks can handle noisy
detection caused by occlusion. In order to apply optimal selection from a
considerable amount of candidates in real-time, we present a novel scoring
function based on a fully convolutional neural network, that shares most
computations on the entire image. Moreover, we adopt a deeply learned
appearance representation, which is trained on large-scale person
re-identification datasets, to improve the identification ability of our
tracker. Extensive experiments show that our tracker achieves real-time and
state-of-the-art performance on a widely used people tracking benchmark.Comment: ICME 201
Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism
In this paper, we propose a CNN-based framework for online MOT. This
framework utilizes the merits of single object trackers in adapting appearance
models and searching for target in the next frame. Simply applying single
object tracker for MOT will encounter the problem in computational efficiency
and drifted results caused by occlusion. Our framework achieves computational
efficiency by sharing features and using ROI-Pooling to obtain individual
features for each target. Some online learned target-specific CNN layers are
used for adapting the appearance model for each target. In the framework, we
introduce spatial-temporal attention mechanism (STAM) to handle the drift
caused by occlusion and interaction among targets. The visibility map of the
target is learned and used for inferring the spatial attention map. The spatial
attention map is then applied to weight the features. Besides, the occlusion
status can be estimated from the visibility map, which controls the online
updating process via weighted loss on training samples with different occlusion
statuses in different frames. It can be considered as temporal attention
mechanism. The proposed algorithm achieves 34.3% and 46.0% in MOTA on
challenging MOT15 and MOT16 benchmark dataset respectively.Comment: Accepted at International Conference on Computer Vision (ICCV) 201
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
Detect-and-Track: Efficient Pose Estimation in Videos
This paper addresses the problem of estimating and tracking human body
keypoints in complex, multi-person video. We propose an extremely lightweight
yet highly effective approach that builds upon the latest advancements in human
detection and video understanding. Our method operates in two-stages: keypoint
estimation in frames or short clips, followed by lightweight tracking to
generate keypoint predictions linked over the entire video. For frame-level
pose estimation we experiment with Mask R-CNN, as well as our own proposed 3D
extension of this model, which leverages temporal information over small clips
to generate more robust frame predictions. We conduct extensive ablative
experiments on the newly released multi-person video pose estimation benchmark,
PoseTrack, to validate various design choices of our model. Our approach
achieves an accuracy of 55.2% on the validation and 51.8% on the test set using
the Multi-Object Tracking Accuracy (MOTA) metric, and achieves state of the art
performance on the ICCV 2017 PoseTrack keypoint tracking challenge.Comment: In CVPR 2018. Ranked first in ICCV 2017 PoseTrack challenge (keypoint
tracking in videos). Code: https://github.com/facebookresearch/DetectAndTrack
and webpage: https://rohitgirdhar.github.io/DetectAndTrack
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