56,796 research outputs found
Target following camera system based on real-time recognition and tracking
A real-time moving target following camera system is presented in this study. The motion of the camera is controlled based on the real-time recognition and tracking of the target object. Scale Invariant Feature Transform (SIFT) based recognition system and Kanade-Lucas-Tomasi (KLT) tracker based tracking system is presented to recognize and track the moving target. SIFT algorithm is slow but efficient in recognizing the objects even though they undergone some affine transformations. KLT tracker algorithm is simple and has reduced computations, hence improves the tracking performance. The analysis is performed in hardware which consists of a camera mounted on a two servo motor setup, one for pan and other for tilt, and an Arduino board capable of handling the movement of two servo motors. As there is hardware implementation, a computationally simplified technique is employed. Since both SIFT and KLT tracker are feature based techniques, we pass the features extracted by SIFT to KLT tracker for simplifying the process. The recognition and tracking tasks are performed in PC and the PWM signals are generated accordingly and sent to servo motors through Arduino. The proposed algorithm is able to track objects even in its absence for a certain while
Flow-Guided Feature Aggregation for Video Object Detection
Extending state-of-the-art object detectors from image to video is
challenging. The accuracy of detection suffers from degenerated object
appearances in videos, e.g., motion blur, video defocus, rare poses, etc.
Existing work attempts to exploit temporal information on box level, but such
methods are not trained end-to-end. We present flow-guided feature aggregation,
an accurate and end-to-end learning framework for video object detection. It
leverages temporal coherence on feature level instead. It improves the
per-frame features by aggregation of nearby features along the motion paths,
and thus improves the video recognition accuracy. Our method significantly
improves upon strong single-frame baselines in ImageNet VID, especially for
more challenging fast moving objects. Our framework is principled, and on par
with the best engineered systems winning the ImageNet VID challenges 2016,
without additional bells-and-whistles. The proposed method, together with Deep
Feature Flow, powered the winning entry of ImageNet VID challenges 2017. The
code is available at
https://github.com/msracver/Flow-Guided-Feature-Aggregation
A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation
This paper presents a novel framework for simultaneously implementing
localization and segmentation, which are two of the most important vision-based
tasks for robotics. While the goals and techniques used for them were
considered to be different previously, we show that by making use of the
intermediate results of the two modules, their performance can be enhanced at
the same time. Our framework is able to handle both the instantaneous motion
and long-term changes of instances in localization with the help of the
segmentation result, which also benefits from the refined 3D pose information.
We conduct experiments on various datasets, and prove that our framework works
effectively on improving the precision and robustness of the two tasks and
outperforms existing localization and segmentation algorithms.Comment: 7 pages, 5 figures.This work has been accepted by ICRA 2019. The demo
video can be found at https://youtu.be/Bkt53dAehj
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