Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods
نویسندگان
چکیده
We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL) and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA) and classification of events, activities and relationships for multi-object trackers (CLEAR MOT)). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.
منابع مشابه
Visual Tracking of Multiple Targets by Multi-Bernoulli Filtering of Background Subtracted Image Data
Most visual multi-target tracking techniques in the literature employ a detection routine to map the image data to point measurements that are usually further processed by a filter. In this paper, we present a visual tracking technique based on a multi-target filtering algorithm that operates directly on the image observations and does not require any detection nor training patterns. Instead, w...
متن کاملDecentralized and Cooperative Multi-Sensor Multi-Target Tracking With Asynchronous Bearing Measurements
Bearings only tracking is a challenging issue with many applications in military and commercial areas. In distributed multi-sensor multi-target bearings only tracking, sensors are far from each other, but are exchanging data using telecommunication equipment. In addition to the general benefits of distributed systems, this tracking system has another important advantage: if the sensors are suff...
متن کاملVisual tracking of numerous targets via multi-Bernoulli filtering of image data
This paper presents a novel Bayesian method to track multiple targets in an image sequence without explicit detection. Our method is formulated based on finite set representation of the multi-target state and the recently developed multi-Bernoulli filter. Experimental results on sport player and cell tracking studies show that our method can automatically track numerous targets, and it outperfo...
متن کاملIntegrating robust likelihoods with Monte-Carlo filters for multi-target tracking
In this paper, a dynamic multi-modal fusion scheme for tracking multiple targets with Monte-Carlo filters is presented, with the goal of achieving robustness by combining complimentary likelihoods based on color and foreground segmentation. The generality of the proposed approach allows defining the measurements on different levels (pixel-, featureand object-space) through dynamic data fusion. ...
متن کاملDevelopment of Multi-target Tracking Technique Based on Background Modeling and Particle Filtering
Based on implementing target tracking by means of particle filtering, a technique framework of tracking target by integrating particle filtering and background modeling is presented. The multi-target tracking (MTT) is classified into 5 modules as background modeling, multi-target tracking, initializing, re-initializing and particle filtering. Firstly, the author models each pixel of the image w...
متن کامل