1,578 research outputs found
Improving Pedestrian Recognition using Incremental Cross Modality Deep Learning
International audienceLate fusion schemes with deep learning classification patterns set up with multi-modality images have an essential role in pedestrian protection systems since they have achieved prominent results in the pedestrian recognition task. In this paper, the late fusion scheme merged with Convolutional Neural Networks (CNN) is investigated for pedestrian recognition based on the Daimler stereo vision data sets. An independent CNN-based classifier for each imaging modality (Intensity, Depth, and Optical Flow) is handled before the fusion of its probabilistic output scores with a Multi-Layer Perceptron which provides the recognition decision. In this paper, we set out to prove that the incremental cross-modality deep learning approach enhances pedestrian recognition performances. It also outperforms state-of-the-art pedestrian classifiers on the Daimler stereo-vision data sets
Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection
The primary bottleneck towards obtaining good recognition performance in IR
images is the lack of sufficient labeled training data, owing to the cost of
acquiring such data. Realizing that object detection methods for the RGB
modality are quite robust (at least for some commonplace classes, like person,
car, etc.), thanks to the giant training sets that exist, in this work we seek
to leverage cues from the RGB modality to scale object detectors to the IR
modality, while preserving model performance in the RGB modality. At the core
of our method, is a novel tensor decomposition method called TensorFact which
splits the convolution kernels of a layer of a Convolutional Neural Network
(CNN) into low-rank factor matrices, with fewer parameters than the original
CNN. We first pretrain these factor matrices on the RGB modality, for which
plenty of training data are assumed to exist and then augment only a few
trainable parameters for training on the IR modality to avoid over-fitting,
while encouraging them to capture complementary cues from those trained only on
the RGB modality. We validate our approach empirically by first assessing how
well our TensorFact decomposed network performs at the task of detecting
objects in RGB images vis-a-vis the original network and then look at how well
it adapts to IR images of the FLIR ADAS v1 dataset. For the latter, we train
models under scenarios that pose challenges stemming from data paucity. From
the experiments, we observe that: (i) TensorFact shows performance gains on RGB
images; (ii) further, this pre-trained model, when fine-tuned, outperforms a
standard state-of-the-art object detector on the FLIR ADAS v1 dataset by about
4% in terms of mAP 50 score.Comment: Accepted to ICCV 2023, LIMIT Workshop. The first two authors
contributed equall
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
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