72,446 research outputs found
Online Domain Adaptation for Multi-Object Tracking
Automatically detecting, labeling, and tracking objects in videos depends
first and foremost on accurate category-level object detectors. These might,
however, not always be available in practice, as acquiring high-quality large
scale labeled training datasets is either too costly or impractical for all
possible real-world application scenarios. A scalable solution consists in
re-using object detectors pre-trained on generic datasets. This work is the
first to investigate the problem of on-line domain adaptation of object
detectors for causal multi-object tracking (MOT). We propose to alleviate the
dataset bias by adapting detectors from category to instances, and back: (i) we
jointly learn all target models by adapting them from the pre-trained one, and
(ii) we also adapt the pre-trained model on-line. We introduce an on-line
multi-task learning algorithm to efficiently share parameters and reduce drift,
while gradually improving recall. Our approach is applicable to any linear
object detector, and we evaluate both cheap "mini-Fisher Vectors" and expensive
"off-the-shelf" ConvNet features. We quantitatively measure the benefit of our
domain adaptation strategy on the KITTI tracking benchmark and on a new dataset
(PASCAL-to-KITTI) we introduce to study the domain mismatch problem in MOT.Comment: To appear at BMVC 201
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection
Over the past decade, deep neural networks (DNNs) have demonstrated
remarkable performance in a variety of applications. As we try to solve more
advanced problems, increasing demands for computing and power resources has
become inevitable. Spiking neural networks (SNNs) have attracted widespread
interest as the third-generation of neural networks due to their event-driven
and low-powered nature. SNNs, however, are difficult to train, mainly owing to
their complex dynamics of neurons and non-differentiable spike operations.
Furthermore, their applications have been limited to relatively simple tasks
such as image classification. In this study, we investigate the performance
degradation of SNNs in a more challenging regression problem (i.e., object
detection). Through our in-depth analysis, we introduce two novel methods:
channel-wise normalization and signed neuron with imbalanced threshold, both of
which provide fast and accurate information transmission for deep SNNs.
Consequently, we present a first spiked-based object detection model, called
Spiking-YOLO. Our experiments show that Spiking-YOLO achieves remarkable
results that are comparable (up to 98%) to those of Tiny YOLO on non-trivial
datasets, PASCAL VOC and MS COCO. Furthermore, Spiking-YOLO on a neuromorphic
chip consumes approximately 280 times less energy than Tiny YOLO and converges
2.3 to 4 times faster than previous SNN conversion methods.Comment: Accepted to AAAI 202
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving
Autonomous driving has harsh requirements of small model size and energy
efficiency, in order to enable the embedded system to achieve real-time
on-board object detection. Recent deep convolutional neural network based
object detectors have achieved state-of-the-art accuracy. However, such models
are trained with numerous parameters and their high computational costs and
large storage prohibit the deployment to memory and computation resource
limited systems. Low-precision neural networks are popular techniques for
reducing the computation requirements and memory footprint. Among them, binary
weight neural network (BWN) is the extreme case which quantizes the float-point
into just bit. BWNs are difficult to train and suffer from accuracy
deprecation due to the extreme low-bit representation. To address this problem,
we propose a knowledge transfer (KT) method to aid the training of BWN using a
full-precision teacher network. We built DarkNet- and MobileNet-based binary
weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car,
pedestrian and cyclist detection. The experimental results show that the
proposed method maintains high detection accuracy while reducing the model size
of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB.Comment: Accepted by ICRA 201
DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels
The impact of soiling on solar panels is an important and well-studied
problem in renewable energy sector. In this paper, we present the first
convolutional neural network (CNN) based approach for solar panel soiling and
defect analysis. Our approach takes an RGB image of solar panel and
environmental factors as inputs to predict power loss, soiling localization,
and soiling type. In computer vision, localization is a complex task which
typically requires manually labeled training data such as bounding boxes or
segmentation masks. Our proposed approach consists of specialized four stages
which completely avoids localization ground truth and only needs panel images
with power loss labels for training. The region of impact area obtained from
the predicted localization masks are classified into soiling types using the
webly supervised learning. For improving localization capabilities of CNNs, we
introduce a novel bi-directional input-aware fusion (BiDIAF) block that
reinforces the input at different levels of CNN to learn input-specific feature
maps. Our empirical study shows that BiDIAF improves the power loss prediction
accuracy by about 3% and localization accuracy by about 4%. Our end-to-end
model yields further improvement of about 24% on localization when learned in a
weakly supervised manner. Our approach is generalizable and showed promising
results on web crawled solar panel images. Our system has a frame rate of 22
fps (including all steps) on a NVIDIA TitanX GPU. Additionally, we collected
first of it's kind dataset for solar panel image analysis consisting 45,000+
images.Comment: Accepted for publication at WACV 201
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