250 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
High pointwise emergence and Katok's conjecture for systems with non-uniform structure
Recently, Kiriki, Nakano and Soma introduced a concept called pointwise
emergence as a new quantitative perspective into the study of non-existence of
averages for dynamical systems. In the present paper, we consider the set of
points with high pointwise emergence for systems with non-uniform structure and
prove that this set carries full topological pressure. For the proof of this
result, we show that such systems have ergodic measures of arbitrary
intermediate pressures
Learning Lightweight Pedestrian Detector with Hierarchical Knowledge Distillation
It remains very challenging to build a pedestrian detection system for real
world applications, which demand for both accuracy and speed. This work
presents a novel hierarchical knowledge distillation framework to learn a
lightweight pedestrian detector, which significantly reduces the computational
cost and still holds the high accuracy at the same time. Following the
`teacher--student' diagram that a stronger, deeper neural network can teach a
lightweight network to learn better representations, we explore multiple
knowledge distillation architectures and reframe this approach as a unified,
hierarchical distillation framework. In particular, the proposed distillation
is performed at multiple hierarchies, multiple stages in a modern detector,
which empowers the student detector to learn both low-level details and
high-level abstractions simultaneously. Experiment result shows that a student
model trained by our framework, with 6 times compression in number of
parameters, still achieves competitive performance as the teacher model on the
widely used pedestrian detection benchmark.Comment: Accepted at ICIP 2019 as Ora
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