3,254 research outputs found
Online Object Tracking with Proposal Selection
Tracking-by-detection approaches are some of the most successful object
trackers in recent years. Their success is largely determined by the detector
model they learn initially and then update over time. However, under
challenging conditions where an object can undergo transformations, e.g.,
severe rotation, these methods are found to be lacking. In this paper, we
address this problem by formulating it as a proposal selection task and making
two contributions. The first one is introducing novel proposals estimated from
the geometric transformations undergone by the object, and building a rich
candidate set for predicting the object location. The second one is devising a
novel selection strategy using multiple cues, i.e., detection score and
edgeness score computed from state-of-the-art object edges and motion
boundaries. We extensively evaluate our approach on the visual object tracking
2014 challenge and online tracking benchmark datasets, and show the best
performance.Comment: ICCV 201
Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds
Accurate detection of 3D objects is a fundamental problem in computer vision
and has an enormous impact on autonomous cars, augmented/virtual reality and
many applications in robotics. In this work we present a novel fusion of neural
network based state-of-the-art 3D detector and visual semantic segmentation in
the context of autonomous driving. Additionally, we introduce
Scale-Rotation-Translation score (SRTs), a fast and highly parameterizable
evaluation metric for comparison of object detections, which speeds up our
inference time up to 20\% and halves training time. On top, we apply
state-of-the-art online multi target feature tracking on the object
measurements to further increase accuracy and robustness utilizing temporal
information. Our experiments on KITTI show that we achieve same results as
state-of-the-art in all related categories, while maintaining the performance
and accuracy trade-off and still run in real-time. Furthermore, our model is
the first one that fuses visual semantic with 3D object detection
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