Planar Object Tracking in the Wild: A Benchmark


Abstract

Planar object tracking plays an important role in computer vision and related fields. While several benchmarks have been constructed for evaluating state-of-the-art algorithms, there is a lack of video sequences captured in the wild rather than in constrained laboratory environment. In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment. In particular, for each object, we shoot seven videos involving various challenging factors, namely scale change, rotation, perspective distortion, motion blur, occlusion, out-of-view, and unconstrained. The ground truth is carefully annotated semi-manually to ensure the quality. Moreover, eleven state-of-the- art algorithms are evaluated on the benchmark using two evaluation metrics, with detailed analysis provided for the evaluation results. We expect the proposed benchmark to benefit future studies on planar object tracking.


Reference


Dataset

All the 210 sequences of the dataset can be downloaded separately for each object using the following links. The groud truth is available at download.


Lottery-1

Lottery-2

Citibank

SmokeFree

Snack

Poster-1

Pizza

Snap

Melts

Pretzel

Poster-2

Painting-1

Painting-2

NoStopping

WalkYourBike

Map-1

StopSign

BusStop

Map-2

Map-3

Fruit

Amish

ShuttleStop

IndegoStation

Woman

Sunoco

Coke

OneWay

Sundae

Burger

Evaluation

All the evaluation results and the Matlab code for generating the following figures are available at download.

Fig. 1 Comparison of evaluated trackers using precision plots. The precision at the threshold tp=5 is used as a representative score.

Fig. 2 The overall performance of trackers in two groups for different challenging factors. For each group, the overall performance is calculated by averaging the performances of trackers within this group. The precision at the threshold tp=5 is used.

Tracker Reference
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Contact

If you have any questions, please contact Pengpeng Liang at pliang AT temple.edu or Haibin Ling at hbling AT temple.edu.