6,797 research outputs found
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
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking
With efficient appearance learning models, Discriminative Correlation Filter
(DCF) has been proven to be very successful in recent video object tracking
benchmarks and competitions. However, the existing DCF paradigm suffers from
two major issues, i.e., spatial boundary effect and temporal filter
degradation. To mitigate these challenges, we propose a new DCF-based tracking
method. The key innovations of the proposed method include adaptive spatial
feature selection and temporal consistent constraints, with which the new
tracker enables joint spatial-temporal filter learning in a lower dimensional
discriminative manifold. More specifically, we apply structured spatial
sparsity constraints to multi-channel filers. Consequently, the process of
learning spatial filters can be approximated by the lasso regularisation. To
encourage temporal consistency, the filter model is restricted to lie around
its historical value and updated locally to preserve the global structure in
the manifold. Last, a unified optimisation framework is proposed to jointly
select temporal consistency preserving spatial features and learn
discriminative filters with the augmented Lagrangian method. Qualitative and
quantitative evaluations have been conducted on a number of well-known
benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and
VOT2018. The experimental results demonstrate the superiority of the proposed
method over the state-of-the-art approaches
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching
In this paper, we propose a novel matching based tracker by investigating the
relationship between template matching and the recent popular correlation
filter based trackers (CFTs). Compared to the correlation operation in CFTs, a
sophisticated similarity metric termed "mutual buddies similarity" (MBS) is
proposed to exploit the relationship of multiple reciprocal nearest neighbors
for target matching. By doing so, our tracker obtains powerful discriminative
ability on distinguishing target and background as demonstrated by both
empirical and theoretical analyses. Besides, instead of utilizing single
template with the improper updating scheme in CFTs, we design a novel online
template updating strategy named "memory filtering" (MF), which aims to select
a certain amount of representative and reliable tracking results in history to
construct the current stable and expressive template set. This scheme is
beneficial for the proposed tracker to comprehensively "understand" the target
appearance variations, "recall" some stable results. Both qualitative and
quantitative evaluations on two benchmarks suggest that the proposed tracking
method performs favorably against some recently developed CFTs and other
competitive trackers.Comment: has been published on IEEE TI
Rain Removal in Traffic Surveillance: Does it Matter?
Varying weather conditions, including rainfall and snowfall, are generally
regarded as a challenge for computer vision algorithms. One proposed solution
to the challenges induced by rain and snowfall is to artificially remove the
rain from images or video using rain removal algorithms. It is the promise of
these algorithms that the rain-removed image frames will improve the
performance of subsequent segmentation and tracking algorithms. However, rain
removal algorithms are typically evaluated on their ability to remove synthetic
rain on a small subset of images. Currently, their behavior is unknown on
real-world videos when integrated with a typical computer vision pipeline. In
this paper, we review the existing rain removal algorithms and propose a new
dataset that consists of 22 traffic surveillance sequences under a broad
variety of weather conditions that all include either rain or snowfall. We
propose a new evaluation protocol that evaluates the rain removal algorithms on
their ability to improve the performance of subsequent segmentation, instance
segmentation, and feature tracking algorithms under rain and snow. If
successful, the de-rained frames of a rain removal algorithm should improve
segmentation performance and increase the number of accurately tracked
features. The results show that a recent single-frame-based rain removal
algorithm increases the segmentation performance by 19.7% on our proposed
dataset, but it eventually decreases the feature tracking performance and
showed mixed results with recent instance segmentation methods. However, the
best video-based rain removal algorithm improves the feature tracking accuracy
by 7.72%.Comment: Published in IEEE Transactions on Intelligent Transportation System
Non-iterative RGB-D-inertial Odometry
This paper presents a non-iterative solution to RGB-D-inertial odometry
system. Traditional odometry methods resort to iterative algorithms which are
usually computationally expensive or require well-designed initialization. To
overcome this problem, this paper proposes to combine a non-iterative front-end
(odometry) with an iterative back-end (loop closure) for the RGB-D-inertial
SLAM system. The main contribution lies in the novel non-iterative front-end,
which leverages on inertial fusion and kernel cross-correlators (KCC) to match
point clouds in frequency domain. Dominated by the fast Fourier transform
(FFT), our method is only of complexity , where is
the number of points. Map fusion is conducted by element-wise operations, so
that both time and space complexity are further reduced. Extensive experiments
show that, due to the lightweight of the proposed front-end, the framework is
able to run at a much faster speed yet still with comparable accuracy with the
state-of-the-arts
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