358,275 research outputs found
Detection of groups of people in images
Tato práce se zabývá dvěma metodami pro detekci objektů v obrazech. První metodou je Viola-Jones, druhou je metoda histogramů orientovaných gradientů. Začátek práce se zabývá teoretickým popisem metod. V dalších částech je prezentována tvorba trénovacích databází, implementace metod v programu RapidMiner a jejich testování. V závěru jsou zhodnoceny výsledky a využití metod pro detekci skupin lidí v databázi obrazů.This work describes two methods for detecting objects in images. The first method is the Viola-Jones, the second is the method of histograms oriented gradients. Start of work deals with the theoretical description of the methods. In the other parts of this work is presented creation of the training databases, implementation methods in the RapidMiner and their testing. In conclusion, the results and the use of methods for detection of groups of people in the database of images are evaluated.
Edges detection in depth images for a gesture recognition application using a Kinect WSN
International audienceThe detection of persons in an image has been the subject of several studies. Most of these works were done on images taken by cameras in visible light (RGB). In this paper, we are interested in people contours detection on the Kinect 3D images. We investigate the application of Gradient approach and optimal filters on depth images. We also use this detection to monitor the person via her gestures. Results show that edge detection of Canny is good for people in both light condition but, the performance of Sobel algorithm was better for the images taken in the dark depths
Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection
Important people detection is to automatically detect the individuals who
play the most important roles in a social event image, which requires the
designed model to understand a high-level pattern. However, existing methods
rely heavily on supervised learning using large quantities of annotated image
samples, which are more costly to collect for important people detection than
for individual entity recognition (eg, object recognition). To overcome this
problem, we propose learning important people detection on partially annotated
images. Our approach iteratively learns to assign pseudo-labels to individuals
in un-annotated images and learns to update the important people detection
model based on data with both labels and pseudo-labels. To alleviate the
pseudo-labelling imbalance problem, we introduce a ranking strategy for
pseudo-label estimation, and also introduce two weighting strategies: one for
weighting the confidence that individuals are important people to strengthen
the learning on important people and the other for neglecting noisy unlabelled
images (ie, images without any important people). We have collected two
large-scale datasets for evaluation. The extensive experimental results clearly
confirm the efficacy of our method attained by leveraging unlabelled images for
improving the performance of important people detection
Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
People detection in single 2D images has improved greatly in recent years.
However, comparatively little of this progress has percolated into multi-camera
multi-people tracking algorithms, whose performance still degrades severely
when scenes become very crowded. In this work, we introduce a new architecture
that combines Convolutional Neural Nets and Conditional Random Fields to
explicitly model those ambiguities. One of its key ingredients are high-order
CRF terms that model potential occlusions and give our approach its robustness
even when many people are present. Our model is trained end-to-end and we show
that it outperforms several state-of-art algorithms on challenging scenes
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