=Paper=
{{Paper
|id=Vol-1901/paper34
|storemode=property
|title=Real-time tracking of multiple objects with locally adaptive correlation filters
|pdfUrl=https://ceur-ws.org/Vol-1901/paper34.pdf
|volume=Vol-1901
|authors=Alexey N. Ruchay,Vitaly I. Kober,Ilya E. Chernoskulov
}}
==Real-time tracking of multiple objects with locally adaptive correlation filters ==
https://ceur-ws.org/Vol-1901/paper34.pdf
Real-time tracking of multiple objects with locally adaptive correlation filters
A.N. Ruchay 1a , 1 , V.I. Kober a1, I.E. Chernoskulov 1a
1
a Chelyabinsk State University, 129 Bratiev Kashirinykh st., Chelyabinsk 454001, Russia
Abstract
A tracking algorithm using locally adaptive correlation filtering is proposed. The algorithm is designed to track multiple objects
with invariance to pose, occlusion, clutter, and illumination variations. The algorithm employs a prediction scheme and composite
correlation filters. The filters are synthesized with the help of an iterative algorithm, which optimizes discrimination capability for
each target. The filters are adapted online to targets changes using information of current and past scene frames. Results obtained
with the proposed algorithm using real-life scenes, are presented and compared with those obtained with state-of-the-art tracking
methods in terms of detection efficiency, tracking accuracy, and speed of processing.
Keywords: tracking; locally adaptive filters; correlation filters; matching.
1. Introduction
Nowadays, object tracking is a widely investigated topic in engineering and computer vision [1, 2]. Video surveillance, vehicle
navigation, human-computer interaction, and robotics are examples of tracking applications [3, 4, 5, 6, 7, 8, 9, 10, 11]. In tracking,
objects are localized in a current frame automatically by applying a detection engine [12, 13, 14, 15]. A main difficulty in object
tracking is that the observed scene is commonly degraded by additive noise, the presence of a cluttered background, geometric
modifications such as pose changing and scaling, gesticulations, and nonuniform illumination. Additionally, eventual occlusions
and real-time requirements are challenges that a modern tracking algorithm must solve.
Object tracking based on correlation-based methods are widely utilized as an attractive alternative to existing tracking algo-
rithms [16, 17, 18]. Correlation filters have a good formal basis, and they can be easily implemented for real-time applications
[19, 20]. Recognition methods involving template matching are not useful in some cases, for instance, when articulation changes
global features like the object outline. So, conventional correlation filters without training may yield a poor performance to recog-
nize objects possessing incomplete information [21, 22, 23]. Adaptive approach to the filter design helps us to synthesize adaptive
filters for object tracking [24, 25].
In this work, we propose an algorithm for object tracking based on locally adaptive correlation filtering. The algorithm is able
to carry out object tracking with a high accuracy in an video without offline training. The objects are selected at the beginning
of the algorithm. Afterwards, a composite correlation filter optimized for distortion tolerant pattern recognition is designed to
recognize the target in the next frame. The impulse responses of optimum correlation filters are used to synthesize composite
filters for distortion invariant object tracking. Two techniques are used to improve the detection performance: adaptive procedure
that achieves a prespecified performance for a typical scene background, and multiple composite filters (bank of composite filters)
when numerous views are available for training. The filter is dynamically adapted to each frame using information of current and
past scene observations.
The paper is organized as follows. Section 2 recalls the optimum composite filter design. Section 3 describes the suggested
algorithm for object tracking by locally adaptive correlation filtering. Computer simulation results obtained with the proposed
algorithm are presented and compared with common algorithms in terms of detection efficiency and location accuracy in section
4. Finally, section 5 presents our conclusions.
2. Composite filter design using optimum correlation filters
We are interested in the design of a correlation filter which is able to recognize an object embedded into a disjoint background
in the scene corrupted with additive noise. The designed filter should be also able to recognize geometrically distorted versions of
the target. Let T = {ti (x, y); i = 1, . . . , N} be an image set containing geometrically distorted versions of the target to be recognized.
The input scene is assumed to be composed by the target t(x, y) embedded into a disjoint background b(x, y) at unknown coordinates
(τ x , τy ), and the whole scene is corrupted with additive noise n(x, y), as follows:
f (x, y) = t(x − τ x , y − τy ) + b(x, y)w(x − τ x , y − τy ) + n(x, y), (1)
where w̄(x, y) is a binary function defined as zero inside the target area, and unity elsewhere. The optimum filter for detecting
the target, in terms of the signal to noise ratio (SNR) and the minimum variance of measurements of location errors (LE), is the
generalized matched filter (GMF) [26], whose frequency response is given by
T (u, v) + µb W(u, v)
H ∗ (u, v) = (2)
Pb (u, v) ⊗ |W(u, v)|2 + Pn (u, v).
1 Corresponding author. Tel.: +7-351-799-7292;
E-mail address: ran@csu.ru
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Image Processing, Geoinformation Technology and Information Security / A.N. Ruchay, V.I. Kober, I.E. Chernoskulov
In (2), T (u, v) and W(u, v) are the Fourier transforms of t(x, y) and w(x, y), respectively; µb is the mean value of the background
b(x, y); Pb (u, v) and Pn (u, v) denote power spectral densities of b0 (x, y) = b(x, y) − µb and n(x, y), respectively. The symbol ⊗
denotes convolution.
Let hi (x, y) be the impulse response of a GMF constructed for the ith available view of the target ti (x, y) in T . Let H =
{hi (x, y); i = 1, . . . , N} be the set of all GMF impulse responses constructed for all training images ti (x, y). Additionally, let
S = {si (x, y); i = 1, . . . , M} be an image set containing M unwanted patterns to be rejected. We want to synthesize a filter capable
to recognize all target views in T and to reject the false patterns in S , by combining the optimum filter templates contained in H,
and by using only a single correlation operation. The required filter p(x, y), can be constructed as follows [26]:
N
∑ N+M
∑
p(x, y) = αi hi (x, y) + αi si (x, y), (3)
i=1 i=N+1
where the coefficients {αi ; i = 1, . . . , N + M} are chosen to satisfy prespecified output values for each pattern in U = T ∪ S . Using
vectormatrix notation, we denote by R a matrix with N + M columns, where each column is the vector version of each element of
U. Let a = [αi ; i = 1, . . . , N + M]T be a vector of coefficients. Thus, (3) can be rewritten as
p = Ra. (4)
Let us denote by
T
u = 1, . . . , 1, 0, . . . , 0 ,
| {z } | {z }
Nones Mzeros
the desired responses to the training patterns, and denote by Q the matrix whose columns are the elements of U. The response
constraints can be expressed as
u = Q+ p, (5)
where superscript + denotes complex conjugate. Substituting (4) into (5), we obtain
u = Q+ Ra.
Thus, the solution for a, is
a = [Q+ R]−1 u. (6)
Finally, substituting (8) into (4), the solution for the composite filter is given by
p = R[Q+ R]−1 u. (7)
Note that the value of the correlation peak when using the filter given in Eq. 7, is expected to be close to unity for true-class
objects, and close to zero for false-class objects.
The discrimination capability (DC) is a measure of the ability of the filter to distinguish a target from unwanted objects; it is
defined by [26]
|cb |2
DC = 1 − t 2 ,
|c |
where cb is the value of the maximum correlation sidelobe in background area and ct is the value of the correlation peak generated
by the target. A DC value close to unity indicates that the filter has a good capability to distinguish between the target and any
false object. Negatives values of the DC indicate that the filter is unable to detect the target. Also, if the obtained DC is greater
than a prespecified threshold (DC > DCth ), then the target is considered as detected and, otherwise, the target is rejected.
3. Object tracking with locally adaptive correlation filtering
In this section we describe the proposed algorithm for object tracking based on composite correlation filtering. The proposed
algorithm is robust to pose changes and appearance modifications of objects, as well as to the presence of scene noise, illumination
changes, and target occlusions.
The algorithm starts with an initialization step where the objects are selected. Next, an optimum correlation filter for reliable
detection and location estimation of the target is designed. Afterwards, a composite locally adaptive correlation filter is synthe-
sized. The proposed algorithm incorporates an automatic re-initialization mechanism that reestablishes the tracking if it fails. The
block diagram of the proposed algorithm is depicted in Fig. 1. The detailed operation steps are explained below.
Step 1: For each object select a small target ti (x, y) from a captured scene frame fi (x, y) containing the object to be tracked.
Step 2: Synthesize an optimum correlation filter hi (x, y) with (2) for reliable detection and location estimation of the target ti (x, y)
in the observed local frame li (x, y).
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Step 3: Synthesize a composite locally adaptive correlation filter pi (x, y) as follows. First, detect and locate the target by hi (x, y)
filter from the observed local frame li (x, y). If the obtained DC is greater than a prespecified threshold (DC > DCrec ), then
the target is considered as successfully detected, ti (x, y) added into the set T and recursion should be stopped. Otherwise,
the target si (x, y) corresponding to a false peak added into the set S . Second, synthesize a composite filter pi (x, y) with the
help of (7). Third, detect and locate the target by pi (x, y) filter from the observed local frame li (x, y) recursively until the
condition DC > DCrec is satisfied.
Step 4: Detect and locate the target in the observed local frame li+1 (x, y) from a new scene frame fi+1 (x, y) by pi (x, y) filter. The
coordinates of the observed local frame li+1 (x, y) are provided by a prediction process that analyzes the motion kinematics
of the target. If the obtained DC is greater than a prespecified threshold (DC > DCth ), then the target is considered as
successfully detected and pi (x, y) filter added to the bank B of composite correlation filters. Otherwise, the target is lost in
the observed local frame li+1 (x, y) and we recursively used the filters from bank B until condition DC > DCcon is satisfied.
The filter from bank B with condition DC > DCcon is used to a new scene frame. If the target is lost in the observed local
frame li+1 (x, y) with help the filters from bank B, then the coordinates of the target is set coordinates of the past scene frame
fi (x, y) and we proceed to a new scene frame fi+2 (x, y).
Begin Synthesize an optimum Select local
correlation ✁lter hi(x, y) frame li(x,y)
Capture a scene
frame fi(x,y) Construct composite filter Add new
pi(x,y) by SDF rejection pattern
Select the target Add different
ti(x,y) versions of target Create a new
Compute DC with frame
rejection pattern
fi(x,y)
from background
Locate next local frame Yes No Locate maximum in
li+1(x, y) by motion DC>DCrec
correlation plane
kinematics in fi(x,y)
No Select previous filter
Compute DC with li+1(x,y) DC > DCth from the bank of
composite correlation
filters
Yes
Add to the bank of
composite Compute DC on li+1(x,y)
correlation filters
No
Yes
Proceed to new
DC > DCcon
frame fi+2(x, y)
Fig. 1. Block diagram of the proposed tracking algorithm based on locally adaptive correlation filtering.
4. Computer simulation
In this section, computer simulation results obtained with the proposed algorithm for object tracking are presented and com-
pared with common algorithms in terms of detection efficiency, tracking accuracy, and speed of processing.
In order to evaluate the performance of our tracker, we conduct experiments on 100 challenging image sequences from Object
Tracking Benchmark (TB-100 database) [27]. These sequences cover most challenging situations in object tracking: Illumination
Variation (IV), Scale Variation (SV), Occlusion (OCC), Deformation (DEF), Motion Blur (MB), Fast Motion (FM), In-Plane
Rotation (IPR), Out-of-Plane Rotation (OPR), Out-of-View (OV), Background Clutters (BC), Low Resolution (LR).
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For comparison, we run 3 state-of-the-art algorithms with the same initial position of the target. The first tracking algorithm
(SURF) [28] is based on matching of local features and descriptors. The second tracking algorithm (STRUCK) predicts the target
location change between frames on the basis of structured learning [29]. The third collaborative tracking algorithm (SCM) is
combined a sparsity-based discriminative classifier and a sparsity-based generative model [30]. The work [27] performed large-
scale experiments to evaluate the performance of recent 33 object-tracking algorithms. Tracking algorithms STRUCK and SCM
perform much better than the others.
For evaluating of detection efficiency we use an evaluation metric of the overlap score. Given a tracked bounding box rt and
the ground-truth bounding extent r0 of a target object, the overlap score is defined as
∥rt ∩ r0 ∥
S = , (8)
∥rt ∪ r0 ∥
where ∩ and ∪ represent the intersection and union operators, respectively, and ∥ · ∥ denotes the number of pixels in a region. This
average overlap score (AOS) can be used as the performance measure. In addition, the overlap scores can be used for determining
whether an algorithm successfully tracks a target in a frame, by testing whether S is larger than a threshold of 0.5. Also we
evaluate the tracking algorithms using the average center location error (ACLE) for all image sequences from database.
Table 1 shows the average overlap score (AOS), the average center location errors (ACLE) and the Average Processing Time
(APT) on a scena for all the tracking algorithms with the overlap threshold of 0.5. The evaluation results show that our proposed
algorithm is faster than the others and more accurate in terms of the average center location errors.
Table 1. Evaluation results of the state-of-the-art STRUCK, SCM, SURF and proposed algorithms by the average overlap score (AOS), the average center location
errors (ACLE), and the Average Processing Time (APT)
Tracker All BC DEF FM IPR IV LR MB OCC OPR OV SV APT ACLE
Proposed 53.3 50.7 51.1 60.0 56.4 43.5 56.7 55.7 44.6 50.5 41.7 51.4 0.2005 68.8
STRUCK 57.5 59.3 52.4 55.6 57.0 59.0 59.1 59.9 55.9 57.3 58.9 57.8 0.2894 61.5
SCM 54.4 61.3 51.5 42.8 51.8 61.1 61.7 45.2 56.8 57.0 56.4 55.8 0.3122 64.8
SURF 35.2 37.4 25.8 41.6 39.7 37.3 23.0 45.4 36.0 34.8 46,.7 33.0 0.1668 276.6
When an object moves fastly on the FM subset, the proposed algorithm performs much better than the others. However, the
proposed algorithm does not perform well in the subset (IV, OCC, OV) due to illumination variation, and partial occlusion of the
target. On the other subsets, the Struck, SCM, and the proposed algorithms outperform other the state-of-the-art algorithms. Fig. 2
shows sample tracking results of the proposed algorithms where the target objects are marked with red rectangles and the actually
tracked objects by the proposed algorithm are marked with green rectangles.
Fig. 2. Results of tracking by proposed algorithm.
5. Conclusion
A tracking algorithm using locally adaptive correlation filtering is proposed. The algorithm is designed to track multiple objects
with invariance to pose, partial occlusion, clutter, and illumination variations. The algorithm employs a prediction scheme and
composite correlation filters. The filters are synthesized with the help of an iterative algorithm, which optimizes discrimination
capability for each target. The filters are adapted online to targets changes using information of current and past scene frames.
The evaluation results show that our proposed algorithm is faster than the others and more accurate in terms of the average center
location errors. On the majority test sets the proposed algorithm performs much better than the state-of-the-art algorithms.
Acknowledgments
This work was supported by the Russian Science Foundation, grant no. 15-19-10010.
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