Automatic Parameter Adaptation for Multi-object Tracking

Chau, Duc Phu; Thonnat, Monique; Brémond, François

doi:10.1007/978-3-642-39402-7_25

Cited by 6 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A possible solution is to create a generic detector adapted for a specific video by tuning some parameters. Previous works for multiple people tracking include [91], [39]. b) MTT with Deep Learning: Deep learning has proven to be a high performance method for classification, detection and many computer visions tasks.…”

Section: Multiple Pedestrian Trackingmentioning

confidence: 99%

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Camara

Bellotto

Coşar

et al. 2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part I of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians. Technologies at these levels are found to be mature and available as foundations for use in high-level systems, such as behaviour modelling, prediction and interaction control.

show abstract

Section: Multiple Pedestrian Trackingmentioning

confidence: 99%

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Camara

Bellotto

Coşar

et al. 2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…The trajectory of the objects are determined by maximizing objects’ trajectory reliability using the Hungarian algorithm [ 21 ]. Since the descriptor weights generally depend on the content of the video being processed, we use the control algorithm proposed by [ 22 ] to tune the weights on an online manner.…”

Section: Knowledge-driven Event Recognitionmentioning

confidence: 99%

Online Recognition of Daily Activities by Color-Depth Sensing and Knowledge Models

Crispim-Junior

Uría

Strumia

et al. 2017

Sensors

View full text Add to dashboard Cite

Visual activity recognition plays a fundamental role in several research fields as a way to extract semantic meaning of images and videos. Prior work has mostly focused on classification tasks, where a label is given for a video clip. However, real life scenarios require a method to browse a continuous video flow, automatically identify relevant temporal segments and classify them accordingly to target activities. This paper proposes a knowledge-driven event recognition framework to address this problem. The novelty of the method lies in the combination of a constraint-based ontology language for event modeling with robust algorithms to detect, track and re-identify people using color-depth sensing (Kinect® sensor). This combination enables to model and recognize longer and more complex events and to incorporate domain knowledge and 3D information into the same models. Moreover, the ontology-driven approach enables human understanding of system decisions and facilitates knowledge transfer across different scenes. The proposed framework is evaluated with real-world recordings of seniors carrying out unscripted, daily activities at hospital observation rooms and nursing homes. Results demonstrated that the proposed framework outperforms state-of-the-art methods in a variety of activities and datasets, and it is robust to variable and low-frame rate recordings. Further work will investigate how to extend the proposed framework with uncertainty management techniques to handle strong occlusion and ambiguous semantics, and how to exploit it to further support medicine on the timely diagnosis of cognitive disorders, such as Alzheimer’s disease.

show abstract

“…첫 번째로 결정적 방법 (deterministic method Klein [6] [6,9] 이나 색 상 분포 모델 [4,7] , HOG (Histogram of oriented gradient)[1], 모양 [8,9] 등을 주요 특징으로 사용하고 있는 것을 알 수 있다.…”

Section: 히 진행되고 있다unclassified

“…기존의 방법에서 색상 정보는 잡음, 객체의 기울 어짐, 부분적 가림 등에 강건하고 계산 속도가 빠른 장점이 있지만 [10] , 조명의 변화에 민감하고 객체의 색상 분포 모델 mean-shift, particle filter Islam [5] Distance Transformation particle filter, cross-correlation Klen [6] Haar-like특징 ada-boost Sidibe [7] 색상 분포 모델, saliency 특징 정보 particle filter Khan [8] 모양, 외형 정보 particle filter, multi-modal mean-shift Chau [9] 거리, 영역, 모양 비율, 색상 히스토그램 Kalman-filter, trajectory filter 표 1. 주요 추적 알고리즘의 특징벡터와 추적 방법 [8] .…”

Section: 히 진행되고 있다unclassified

Object Tracking Using Particle Filters in Moving Camera

Ko¹,

Nam²,

Kwak³

2012

The Journal of Korean Institute of Communications and Informati

View full text Add to dashboard Cite

This paper proposes a new real-time object tracking algorithm using particle filters with color and texture features in moving CCD camera images. If the user selects an initial object, this region is declared as a target particle and an initial state is modeled. Then, N particles are generated based on random distribution and CS-LBP (Centre Symmetric Local Binary Patterns) for texture model and weighted color distribution is modeled from each particle. For observation likelihoods estimation, Bhattacharyya distance between particles and their feature models are calculated and this observation likelihoods are used for weights of individual particles. After weights estimation, a new particle which has the maximum weight is selected and new particles are re-sampled using the maximum particle. For performance comparison, we tested a few combinations of features and particle filters. The proposed algorithm showed best object tracking performance when we used color and texture model simultaneously for likelihood estimation.

show abstract

Automatic Parameter Adaptation for Multi-object Tracking

Cited by 6 publications

References 11 publications

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Online Recognition of Daily Activities by Color-Depth Sensing and Knowledge Models

Object Tracking Using Particle Filters in Moving Camera

Contact Info

Product

Resources

About