10 research outputs found
Learning Background-Aware Correlation Filters for Visual Tracking
Correlation Filters (CFs) have recently demonstrated excellent performance in
terms of rapidly tracking objects under challenging photometric and geometric
variations. The strength of the approach comes from its ability to efficiently
learn - "on the fly" - how the object is changing over time. A fundamental
drawback to CFs, however, is that the background of the object is not be
modelled over time which can result in suboptimal results. In this paper we
propose a Background-Aware CF that can model how both the foreground and
background of the object varies over time. Our approach, like conventional CFs,
is extremely computationally efficient - and extensive experiments over
multiple tracking benchmarks demonstrate the superior accuracy and real-time
performance of our method compared to the state-of-the-art trackers including
those based on a deep learning paradigm
Correlation Filters with Limited Boundaries
Correlation filters take advantage of specific properties in the Fourier
domain allowing them to be estimated efficiently: O(NDlogD) in the frequency
domain, versus O(D^3 + ND^2) spatially where D is signal length, and N is the
number of signals. Recent extensions to correlation filters, such as MOSSE,
have reignited interest of their use in the vision community due to their
robustness and attractive computational properties. In this paper we
demonstrate, however, that this computational efficiency comes at a cost.
Specifically, we demonstrate that only 1/D proportion of shifted examples are
unaffected by boundary effects which has a dramatic effect on
detection/tracking performance. In this paper, we propose a novel approach to
correlation filter estimation that: (i) takes advantage of inherent
computational redundancies in the frequency domain, and (ii) dramatically
reduces boundary effects. Impressive object tracking and detection results are
presented in terms of both accuracy and computational efficiency.Comment: 8 pages, 6 figures, 2 table
MULTI-CHANNEL CORRELATION FILTERS WITH LIMITED BOUNDARIES: THEORY AND APPLICATIONS
Ph.DDOCTOR OF PHILOSOPH
Deep-LK for Efficient Adaptive Object Tracking
In this paper we present a new approach for efficient regression based object
tracking which we refer to as Deep- LK. Our approach is closely related to the
Generic Object Tracking Using Regression Networks (GOTURN) framework of Held et
al. We make the following contributions. First, we demonstrate that there is a
theoretical relationship between siamese regression networks like GOTURN and
the classical Inverse-Compositional Lucas & Kanade (IC-LK) algorithm. Further,
we demonstrate that unlike GOTURN IC-LK adapts its regressor to the appearance
of the currently tracked frame. We argue that this missing property in GOTURN
can be attributed to its poor performance on unseen objects and/or viewpoints.
Second, we propose a novel framework for object tracking - which we refer to as
Deep-LK - that is inspired by the IC-LK framework. Finally, we show impressive
results demonstrating that Deep-LK substantially outperforms GOTURN.
Additionally, we demonstrate comparable tracking performance to current state
of the art deep-trackers whilst being an order of magnitude (i.e. 100 FPS)
computationally efficient
Dense Feature Aggregation and Pruning for RGBT Tracking
How to perform effective information fusion of different modalities is a core
factor in boosting the performance of RGBT tracking. This paper presents a
novel deep fusion algorithm based on the representations from an end-to-end
trained convolutional neural network. To deploy the complementarity of features
of all layers, we propose a recursive strategy to densely aggregate these
features that yield robust representations of target objects in each modality.
In different modalities, we propose to prune the densely aggregated features of
all modalities in a collaborative way. In a specific, we employ the operations
of global average pooling and weighted random selection to perform channel
scoring and selection, which could remove redundant and noisy features to
achieve more robust feature representation. Experimental results on two RGBT
tracking benchmark datasets suggest that our tracker achieves clear
state-of-the-art against other RGB and RGBT tracking methods.Comment: arXiv admin note: text overlap with arXiv:1811.0985
Tracking Groups of People in Presence of Occlusion
Abstract-This paper addresses the problem of people group tracking in presence of occlusion as people form groups, interact within groups or leave groups. Foreground objects (a person or a group of people) from two consecutive frames are matched based on appearance (RGB histogram) and object location (2D region) similarity. While tracking, this method determines and handles some events such as objects merging and splitting using forward and backward matching matrices. The experimental results show that the proposed algorithm is efficient to track group of people in cluttered and complex environments even when total or partial occlusion occurs
2012b. Inter-modality Face Sketch Recognition
Abstract-Automatic face sketch recognition plays an important role in law enforcement. Recently, various methods have been proposed to address the problem of face sketch recognition by matching face photos and sketches, which are of different modalities. However, their performance is strongly affected by the modality difference between sketches and photos. In this paper, we propose a new face descriptor based on gradient orientations to reduce the modality difference in feature extraction stage, called Histogram of Averaged Oriented Gradients (HAOG). Experiments on CUFS database show that the new descriptor outperforms the state-of-the-art approaches
2012a. Face sketch recognition by Local Radon Binary Pattern: LRBP
ABSTRACT In this paper, we propose a new face descriptor to directly match face photos and sketches of different modalities, called Local Radon Binary Pattern (LRBP). LRBP is inspired by the fact that the shape of a face photo and its corresponding sketch is similar, even when the sketch is exaggerated by an artist. Therefore, the shape of face can be exploited to compute features which are robust against modality differences between face photo and sketch. In LRBP framework, the characteristics of face shape are captured by transforming face image into Radon space. Then, micro-information of face shape in new space is encoded by Local Binary Pattern (LBP). Finally, LRBP is computed by concatenating histograms of local LBPs. In order to capture both local and global characteristics of face shape, LRBP is extracted in a spatial pyramid fashion. Experiments on CUFS and CUFSF datasets indicate the efficiency of LRBP for face sketch recognition
The Sixth Visual Object Tracking VOT2018 Challenge Results
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).Funding agencies: Slovenian research agencySlovenian Research Agency - Slovenia [P2-0214, P2-0094, J2-8175]; Czech Science FoundationGrant Agency of the Czech Republic [GACR P103/12/G084]; WASP; VR (EMC2); SSF (SymbiCloud); SNIC; AIT Strategic Research Programme 2017 Visua</p