Real time multiple objects tracking based on a bio-inspired processing cascade architecture

Gómez-Rodríguez, F.; Miró-Amarante, Lourdes; Díaz-del-Río, Fernando; Linares-Barranco, A.; Robotics, G. Jimenez.

doi:10.1109/iscas.2010.5537277

Cited by 15 publications

(16 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other works focused on event-based algorithms for traffic monitoring [23], [24] from a static sensor point-of-view, and consequently, tracking can be treated without background modeling as only dynamic objects are picked out by the static event camera. Similarly, the robot goalie application [25] and [26] take advantage of the stationary DVS camera for tracking multiple objects. Recently, [27] proposed an event-based algorithm that can perform tasks such as detection and tracking designed specifically for space situational awareness applications.…”

Section: A Related Workmentioning

confidence: 99%

e-TLD: Event-Based Framework for Dynamic Object Tracking

Ramesh

Zhang

Yang

et al. 2021

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

This paper presents a long-term object tracking framework with a moving event camera under general tracking conditions. A first of its kind for these revolutionary cameras, the tracking framework uses a discriminative representation for the object with online learning, and detects and re-tracks the object when it comes back into the field-of-view. One of the key novelties is the use of an event-based local sliding window technique that tracks reliably in scenes with cluttered and textured background. In addition, Bayesian bootstrapping is used to assist real-time processing and boost the discriminative power of the object representation. On the other hand, when the object re-enters the field-of-view of the camera, a data-driven, global sliding window detector locates the object for subsequent tracking. Extensive experiments demonstrate the ability of the proposed framework to track and detect arbitrary objects of various shapes and sizes, including dynamic objects such as a human. This is a significant improvement compared to earlier works that simply track objects as long as they are visible under simpler background settings. Using the ground truth locations for five different objects under three motion settings, namely translation, rotation and 6-DOF, quantitative measurement is reported for the event-based tracking framework with critical insights on various performance issues. Finally, real-time implementation in C++ highlights tracking ability under scale, rotation, view-point and occlusion scenarios in a lab setting.

show abstract

Section: A Related Workmentioning

confidence: 99%

e-TLD: Event-Based Framework for Dynamic Object Tracking

Ramesh

Zhang

Yang

et al. 2021

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

show abstract

“…Under this project we also developed an improved dynamic vision sensor [3], an AER convolution chip [20], a spike based filter for object detection and tracking [21], a scalable mesh network multi-PCB technique of spike convolutions for cortex operation emulation [9], and spike based motor controllers (proportional-integral-derivative [15] and neuro-inspired open-loop VITE [16]). The AER-Node board was designed to allow multi-PCB communication with conventional parallel-handshaked-AER chips (retinas and convolutions) or robots with the adequate motor interfaces.…”

Section: Aer-node Pcbmentioning

confidence: 99%

An AER handshake-less modular infrastructure PCB with x8 2.5Gbps LVDS serial links

Iakymchuk

Rosado

Serrano-Gotarredona

et al. 2014

2014 IEEE International Symposium on Circuits and Systems (ISCAS)

Self Cite

View full text Add to dashboard Cite

Nowadays spike-based brain processing emulation is taking off. Several EU and others worldwide projects are demonstrating this, like SpiNNaker, BrainScaleS, FACETS, or NeuroGrid. The larger the brain process emulation on silicon is, the higher the communication performance of the hosting platforms has to be. Many times the bottleneck of these system implementations is not on the performance inside a chip or a board, but in the communication between boards. This paper describes a novel modular Address-Event-Representation (AER) FPGA-based (Spartan6) infrastructure PCB (the AER-Node board) with 2.5Gbps LVDS high speed serial links over SATA cables that offers a peak performance of 32-bit 62.5Meps (Mega events per second) on board-to-board communications. The board allows back compatibility with parallel AER devices supporting up to x2 28-bit parallel data with asynchronous handshake. These boards also allow modular expansion functionality through several daughter boards. The paper is focused on describing in detail the LVDS serial interface and presenting its performance.

show abstract

“…The majority of groups is working on neuromorphic sensors like e.g. silicon retinas and visual processing systems (Netter and Franceschini, 2002;Delbrück and Liu, 2004;Serrano-Gotarredona et al, 2006;Merolla and Boahen, 2006;Fu et al, 2008;Gomez-Rodriguez et al, 2010) or motor control in robotics (Lewis et al, 2000). The requirement of communication with the environment is one important reason for the fact that nearly all neuromorphic devices reported so far are designed to operate in real-time.…”

Section: Neuromorphic Hardwarementioning

confidence: 99%

A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems

et al. 2011

View full text Add to dashboard Cite

In this article, we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results.

show abstract

Real time multiple objects tracking based on a bio-inspired processing cascade architecture

Cited by 15 publications

References 7 publications

e-TLD: Event-Based Framework for Dynamic Object Tracking

e-TLD: Event-Based Framework for Dynamic Object Tracking

An AER handshake-less modular infrastructure PCB with x8 2.5Gbps LVDS serial links

A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems

Contact Info

Product

Resources

About