17 research outputs found
Measuring the Accuracy of Object Detectors and Trackers
The accuracy of object detectors and trackers is most commonly evaluated by
the Intersection over Union (IoU) criterion. To date, most approaches are
restricted to axis-aligned or oriented boxes and, as a consequence, many
datasets are only labeled with boxes. Nevertheless, axis-aligned or oriented
boxes cannot accurately capture an object's shape. To address this, a number of
densely segmented datasets has started to emerge in both the object detection
and the object tracking communities. However, evaluating the accuracy of object
detectors and trackers that are restricted to boxes on densely segmented data
is not straightforward. To close this gap, we introduce the relative
Intersection over Union (rIoU) accuracy measure. The measure normalizes the IoU
with the optimal box for the segmentation to generate an accuracy measure that
ranges between 0 and 1 and allows a more precise measurement of accuracies.
Furthermore, it enables an efficient and easy way to understand scenes and the
strengths and weaknesses of an object detection or tracking approach. We
display how the new measure can be efficiently calculated and present an
easy-to-use evaluation framework. The framework is tested on the DAVIS and the
VOT2016 segmentations and has been made available to the community.Comment: 10 pages, 7 Figure
uMoDT: an unobtrusive multi-occupant detection and tracking using robust Kalman filter for real-time activity recognition
Discriminative Correlation Filter Tracker with Channel and Spatial Reliability
Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This both allows to enlarge the search region and improves tracking of non-rectangular objects. Reliability scores reflect channel-wise quality of the learned filters and are used as feature weighting coefficients in localization. Experimentally, with only two simple standard feature sets, HoGs and colornames, the novel CSR-DCF method---DCF with channel and spatial reliability---achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB100. The CSR-DCF runs close to real-time on a CPU
Discriminative Correlation Filter Tracker with Channel and Spatial Reliability
Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This both allows to enlarge the search region and improves tracking of non-rectangular objects. Reliability scores reflect channel-wise quality of the learned filters and are used as feature weighting coefficients in localization. Experimentally, with only two simple standard feature sets, HoGs and colornames, the novel CSR-DCF method—DCF with channel and spatial reliability—achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB100. The CSR-DCF runs close to real-time on a CPU
Efficient scale estimation methods using lightweight deep convolutional neural networks for visual tracking
An Exploration of Target-Conditioned Segmentation Methods for Visual Object Trackers
Visual object tracking is the problem of predicting a target object\u2019s state in a video. Generally, bounding-boxes have been used to represent states, and a surge of effort has been spent by the community to produce efficient causal algorithms capable of locating targets with such representations. As the field is moving towards binary segmentation masks to define objects more precisely, in this paper we propose to extensively explore target-conditioned segmentation methods available in the computer vision community, in order to transform any bounding-box tracker into a segmentation tracker. Our analysis shows that such methods allow trackers to compete with recently proposed segmentation trackers, while performing quasi real-time