22 research outputs found
Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification
Online multi-object tracking is a fundamental problem in time-critical video
analysis applications. A major challenge in the popular tracking-by-detection
framework is how to associate unreliable detection results with existing
tracks. In this paper, we propose to handle unreliable detection by collecting
candidates from outputs of both detection and tracking. The intuition behind
generating redundant candidates is that detection and tracks can complement
each other in different scenarios. Detection results of high confidence prevent
tracking drifts in the long term, and predictions of tracks can handle noisy
detection caused by occlusion. In order to apply optimal selection from a
considerable amount of candidates in real-time, we present a novel scoring
function based on a fully convolutional neural network, that shares most
computations on the entire image. Moreover, we adopt a deeply learned
appearance representation, which is trained on large-scale person
re-identification datasets, to improve the identification ability of our
tracker. Extensive experiments show that our tracker achieves real-time and
state-of-the-art performance on a widely used people tracking benchmark.Comment: ICME 201
Transformer Networks for Trajectory Forecasting
Most recent successes on forecasting the people motion are based on LSTM
models and all most recent progress has been achieved by modelling the social
interaction among people and the people interaction with the scene. We question
the use of the LSTM models and propose the novel use of Transformer Networks
for trajectory forecasting. This is a fundamental switch from the sequential
step-by-step processing of LSTMs to the only-attention-based memory mechanisms
of Transformers. In particular, we consider both the original Transformer
Network (TF) and the larger Bidirectional Transformer (BERT), state-of-the-art
on all natural language processing tasks. Our proposed Transformers predict the
trajectories of the individual people in the scene. These are "simple" model
because each person is modelled separately without any complex human-human nor
scene interaction terms. In particular, the TF model without bells and whistles
yields the best score on the largest and most challenging trajectory
forecasting benchmark of TrajNet. Additionally, its extension which predicts
multiple plausible future trajectories performs on par with more engineered
techniques on the 5 datasets of ETH + UCY. Finally, we show that Transformers
may deal with missing observations, as it may be the case with real sensor
data. Code is available at https://github.com/FGiuliari/Trajectory-Transformer.Comment: 18 pages, 3 figure
A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables
It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications
Multiple-Vehicle Tracking in the Highway Using Appearance Model and Visual Object Tracking
In recent decades, due to the groundbreaking improvements in machine vision,
many daily tasks are performed by computers. One of these tasks is
multiple-vehicle tracking, which is widely used in different areas such as
video surveillance and traffic monitoring. This paper focuses on introducing an
efficient novel approach with acceptable accuracy. This is achieved through an
efficient appearance and motion model based on the features extracted from each
object. For this purpose, two different approaches have been used to extract
features, i.e. features extracted from a deep neural network, and traditional
features. Then the results from these two approaches are compared with
state-of-the-art trackers. The results are obtained by executing the methods on
the UA-DETRACK benchmark. The first method led to 58.9% accuracy while the
second method caused up to 15.9%. The proposed methods can still be improved by
extracting more distinguishable features