280 research outputs found
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
Object detection systems based on the deep convolutional neural network (CNN)
have recently made ground- breaking advances on several object detection
benchmarks. While the features learned by these high-capacity neural networks
are discriminative for categorization, inaccurate localization is still a major
source of error for detection. Building upon high-capacity CNN architectures,
we address the localization problem by 1) using a search algorithm based on
Bayesian optimization that sequentially proposes candidate regions for an
object bounding box, and 2) training the CNN with a structured loss that
explicitly penalizes the localization inaccuracy. In experiments, we
demonstrated that each of the proposed methods improves the detection
performance over the baseline method on PASCAL VOC 2007 and 2012 datasets.
Furthermore, two methods are complementary and significantly outperform the
previous state-of-the-art when combined.Comment: CVPR 201
Comparison of fusion methods for thermo-visual surveillance tracking
In this paper, we evaluate the appearance tracking performance of multiple fusion schemes that combine information from standard CCTV and thermal infrared spectrum video for the tracking of surveillance objects, such as people, faces, bicycles and vehicles. We show results on numerous real world multimodal surveillance sequences, tracking challenging objects whose appearance changes rapidly. Based on these results we can determine the most promising fusion scheme
Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering
In this paper, we propose saliency driven image multiscale nonlinear diffusion filtering. The resulting scale space in general preserves or even enhances semantically important structures such as edges, lines, or flow-like structures in the foreground, and inhibits and smoothes clutter in the background. The image is classified using multiscale information fusion based on the original image, the image at the final scale at which the diffusion process converges, and the image at a midscale. Our algorithm emphasizes the foreground features, which are important for image classification. The background image regions, whether considered as contexts of the foreground or noise to the foreground, can be globally handled by fusing information from different scales. Experimental tests of the effectiveness of the multiscale space for the image classification are conducted on the following publicly available datasets: 1) the PASCAL 2005 dataset; 2) the Oxford 102 flowers dataset; and 3) the Oxford 17 flowers dataset, with high classification rates
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
Standardized benchmarks have been crucial in pushing the performance of
computer vision algorithms, especially since the advent of deep learning.
Although leaderboards should not be over-claimed, they often provide the most
objective measure of performance and are therefore important guides for
research. We present MOTChallenge, a benchmark for single-camera Multiple
Object Tracking (MOT) launched in late 2014, to collect existing and new data,
and create a framework for the standardized evaluation of multiple object
tracking methods. The benchmark is focused on multiple people tracking, since
pedestrians are by far the most studied object in the tracking community, with
applications ranging from robot navigation to self-driving cars. This paper
collects the first three releases of the benchmark: (i) MOT15, along with
numerous state-of-the-art results that were submitted in the last years, (ii)
MOT16, which contains new challenging videos, and (iii) MOT17, that extends
MOT16 sequences with more precise labels and evaluates tracking performance on
three different object detectors. The second and third release not only offers
a significant increase in the number of labeled boxes but also provide labels
for multiple object classes beside pedestrians, as well as the level of
visibility for every single object of interest. We finally provide a
categorization of state-of-the-art trackers and a broad error analysis. This
will help newcomers understand the related work and research trends in the MOT
community, and hopefully shed some light on potential future research
directions.Comment: Accepted at IJC
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