11,351 research outputs found
Grid Loss: Detecting Occluded Faces
Detection of partially occluded objects is a challenging computer vision
problem. Standard Convolutional Neural Network (CNN) detectors fail if parts of
the detection window are occluded, since not every sub-part of the window is
discriminative on its own. To address this issue, we propose a novel loss layer
for CNNs, named grid loss, which minimizes the error rate on sub-blocks of a
convolution layer independently rather than over the whole feature map. This
results in parts being more discriminative on their own, enabling the detector
to recover if the detection window is partially occluded. By mapping our loss
layer back to a regular fully connected layer, no additional computational cost
is incurred at runtime compared to standard CNNs. We demonstrate our method for
face detection on several public face detection benchmarks and show that our
method outperforms regular CNNs, is suitable for realtime applications and
achieves state-of-the-art performance.Comment: accepted to ECCV 201
A practical multirobot localization system
We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with a millimeter precision. In addition, we present the method's mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at \emph{http://purl.org/robotics/whycon}; so, it can be used as an enabling technology for various mobile robotic problems
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