653 research outputs found
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
Efficient High-Resolution Template Matching with Vector Quantized Nearest Neighbour Fields
Template matching is a fundamental problem in computer vision and has
applications in various fields, such as object detection, image registration,
and object tracking. The current state-of-the-art methods rely on
nearest-neighbour (NN) matching in which the query feature space is converted
to NN space by representing each query pixel with its NN in the template
pixels. The NN-based methods have been shown to perform better in occlusions,
changes in appearance, illumination variations, and non-rigid transformations.
However, NN matching scales poorly with high-resolution data and high feature
dimensions. In this work, we present an NN-based template-matching method which
efficiently reduces the NN computations and introduces filtering in the NN
fields to consider deformations. A vector quantization step first represents
the template with features, then filtering compares the template and query
distributions over the features. We show that state-of-the-art performance
was achieved in low-resolution data, and our method outperforms previous
methods at higher resolution showing the robustness and scalability of the
approach
FACE IMAGE RECOGNITION BASED ON PARTIAL FACE MATCHING USING GENETIC ALGORITHM
In various real-world face recognition applications such as forensics and surveillance, only
partial face image is available. Hence, template matching and recognition are strongly needed. In this
paper, a genetic algorithm to match a pattern of an image and then recognize this image by this pattern is
proposed. This algorithm can use any pattern of an image such as eye, mouth or ear to recognize the
image. The proposed genetic algorithm uses a small length chromosome to decrease the search space, and
hence the results could be obtained in a short time. Two datasets were used to test the proposed method
which are AR Face database and LFW database of face, the overall matching and recognition accuracy
were calculated based on conducting sequences of experiments on random sub-datasets, where the overall
matching and recognition accuracy was 91.7% and 90% respectively. The results of the proposed
algorithm demonstrate the robustness and efficiency compared with other state-of-the-art algorithm
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