3D Tracking of Multiple Objects with Identical Appearance Using RGB-D Input

Ren, Carl Yuheng; Prisacariu, Victor Adrian; Kaehler, Olaf; Reid, Ian; Murray, David

doi:10.1109/3dv.2014.39

Cited by 18 publications

(12 citation statements)

References 14 publications

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“…Moreover, the particle filter approaches [6,13] extends existing RGB trackers to include the depth data. Another work [17] uses level-set optimization with appearance and physical constraints to handle occlusions from interacting objects; but, they only conduct their experiments on textureless objects with simple geometric structure such as prisms or spheres. Among the RGB-D methods [6,10,13,17], it is common to implement them in GPU for real-time tracking.…”

Section: Introductionmentioning

confidence: 99%

“…Another work [17] uses level-set optimization with appearance and physical constraints to handle occlusions from interacting objects; but, they only conduct their experiments on textureless objects with simple geometric structure such as prisms or spheres. Among the RGB-D methods [6,10,13,17], it is common to implement them in GPU for real-time tracking. In effect, their runtime depends on the type of GPU that they use, which creates a problem to track more objects while still keeping the real-time performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Versatile Learning-Based 3D Temporal Tracker: Scalable, Robust, Online

Tan

Tombari²,

Ilić

et al. 2015

2015 IEEE International Conference on Computer Vision (ICCV)

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This paper proposes a temporal tracking algorithm based on Random Forest that uses depth images to estimate and track the 3D pose of a rigid object in real-time. Compared to the state of the art aimed at the same goal, our algorithm holds important attributes such as high robustness against holes and occlusion, low computational cost of both learning and tracking stages, and low memory consumption. These are obtained (a) by a novel formulation of the learning strategy, based on a dense sampling of the camera viewpoints and learning independent trees from a single image for each camera view; as well as, (b) by an insightful occlusion handling strategy that enforces the forest to recognize the object's local and global structures. Due to these attributes, we report state-of-the-art tracking accuracy on benchmark datasets, and accomplish remarkable scalability with the number of targets, being able to simultaneously track the pose of over a hundred objects at 30 fps with an off-the-shelf CPU. In addition, the fast learning time enables us to extend our algorithm as a robust online tracker for model-free 3D objects under different viewpoints and appearance changes as demonstrated by the experiments.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Versatile Learning-Based 3D Temporal Tracker: Scalable, Robust, Online

Tan

Tombari²,

Ilić

et al. 2015

2015 IEEE International Conference on Computer Vision (ICCV)

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show abstract

“…methods have then been further extended to depth images for simultaneous tracking and reconstruction [20,19]. Indeed, exploiting RGB-D is beneficial since image-based contour information and depth maps are complimentary cues, one being focused on object borders, the other on object-internal regions.…”

Section: Introductionmentioning

confidence: 99%

“…This has been exploited for 3D object detection and tracking [16,9,12], as well as to improve camera tracking in planar indoor environments [32]. From a computational perspective, several state-of-theart trackers leverage the GPU for real-time performance [20,19,29]. Nevertheless, there is a strong interest towards decreasing the computational burden and generally avoiding GPU usage, motivated by the fact that many relevant applications require trackers to be light-weight [11,17].…”

Section: Introductionmentioning

confidence: 99%

Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core

Kehl

Tombari²,

Ilić

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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We present a novel method to track 3D models in color and depth data. To this end, we introduce approximations that accelerate the state-of-the-art in region-based tracking by an order of magnitude while retaining similar accuracy. Furthermore, we show how the method can be made more robust in the presence of depth data and consequently formulate a new joint contour and ICP tracking energy. We present better results than the state-of-the-art while being much faster then most other methods and achieving all of the above on a single CPU core.

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“…The second is the hard physical constraint that multiple rigid bodies may touch but may not occupy the same 3D space. These two issues are addressed here in an RGB-D tracker that we originally proposed in Ren et al (2014). This tracker can recover the 3D pose of multiple objects with identical appearance, while preventing them from intersecting.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions

et al. 2017

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We describe a novel probabilistic framework for real-time tracking of multiple objects from combined depthcolour imagery. Object shape is represented implicitly using 3D signed distance functions. Probabilistic generative models based on these functions are developed to account for the observed RGB-D imagery, and tracking is posed as a maximum a posteriori problem. We present first a method suited to tracking a single rigid 3D object, and then generalise this to multiple objects by combining distance functions into a shape union in the frame of the camera. This second model accounts for similarity and proximity between objects, and leads to robust real-time tracking without recourse to bolt-on or ad-hoc collision detection.

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3D Tracking of Multiple Objects with Identical Appearance Using RGB-D Input

Cited by 18 publications

References 14 publications

A Versatile Learning-Based 3D Temporal Tracker: Scalable, Robust, Online

A Versatile Learning-Based 3D Temporal Tracker: Scalable, Robust, Online

Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core

Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions

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