Animal recognition in the Mojave Desert: Vision tools for field biologists

Wilber, Michael J.; Scheirer, Walter J.; Leitner, Philip; Heflin, Brian; Zott, James P.; Reinke, Donald L.; Delaney, David K.; Boult, Terry

doi:10.1109/wacv.2013.6475020

Cited by 32 publications

(27 citation statements)

References 13 publications

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“…Many works were published focusing in object detection and classification in surveillance systems, traffic monitoring, human-machine interface, smart applications and different security solutions [1][2][3][4]. But there are only a few publications, which are focusing on detection and classification of wild animals [5], [6]. This was one of the motivations for creation an Automatic System For Animal Recognition (ASFAR system).…”

Section: Introductionmentioning

confidence: 99%

A Novel System for Non-Invasive Method of Animal Tracking and Classification in Designated Area Using Intelligent Camera System

Kamencay¹,

Benčo²,

Matúška³

et al. 2016

RADIOENGINEERING

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Section: Introductionmentioning

confidence: 99%

A Novel System for Non-Invasive Method of Animal Tracking and Classification in Designated Area Using Intelligent Camera System

Kamencay¹,

Benčo²,

Matúška³

et al. 2016

RADIOENGINEERING

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“…In fact, there is only a few body of works focusing on wildlife surveillance domain [25,134,18]. The benefit of understanding this domain is two-fold: (1) Unlike human surveillance, studying wildlife surveillance will not offend public privacy.…”

Section: Albert Einstein 1879 -1955mentioning

confidence: 99%

“…In [134], the authors work on animal recognition where the data are collected from the Mojave desert.…”

Section: Video Surveillance In Novel Domainsmentioning

confidence: 99%

“…There have been several works regarding the usage of computer vision tools for the assistance of field work studies such as [25,134,18]. In [25], authors analyse the data collected in the wild of Kenya for animal identification.…”

Section: Introductionmentioning

confidence: 99%

“…They use the standard SIFT features to perform the task. In [134], the authors work on animal recognition where the data are collected from the Mojave desert. They also use the simple SIFT-based method to assist the field biologist for an animal population study.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the frontier of smart video surveillance: novel domains and fine-grain event understanding

Zhang¹

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Video surveillance plays an important role in our times. It is a great help in reducing the crime rate, and it can also help monitoring the status of facilities or animals. The contribution video surveillance is significant especially regarding the public and home safety.However, the performance of video surveillance system is limited by human factors such as fatigue, time efficiency, and human resources. It would be beneficial for all if fully automatic video surveillance systems are employed to do the job. With this aim, many commercial products and research prototypes have been developed for the automation of video surveillance. Although progress has been made, the performance and ability of the emerging automatic surveillance systems are still not satisfying regarding two major unsolved problems: 1) The ability to work in multiple domains; 2)The ability to understand and describe the events like the human.On the one hand, special domains such as the thermal camera or hidden camera in the wild forest are seldom considered today. We can develop great potential usage for these novel domains. For example, we can catch the thief even if the light is turned off at night with a thermal face recognition surveillance system. We can also study surveillance system with wildlife without worrying about privacy issues with the human. And surveillance in wildlife domain is a great help to zoologists and biologists. In a short word, the ability to work under different circumstances is an important sign of an advanced surveillance system and it can have numerous benefits.On the other hand, existing surveillance systems lack the event understanding and description ability. For example, current commercialised smart surveillance systems can only detect motion changes or human faces. They are far away from the ideal system that can understand the meaning of the motion in the perspective of events. Even the research prototypes which have the abilities of event recognition and anomaly detection are not able to deal with unseen events or understand relationships between roles in an event. For instance, when the detector of the smart surveillance system detected keywords "person", "baby" and "feed", we do not want the system to report "a person feeding a baby" when the actual screen is a scene where the baby is trying to share the food.This thesis aims to explore the frontier of smart video surveillance systems by investigating the above-mentioned two problems. In this thesis, several methods are proposed to study video surveillance in various domains such as crowded scene domain, and more importantly, in novel domains that are barely investigated such as thermal imaging and wildlife domain. Also, novel methods of event analysis and video description are proposed to enhance the efficiency of human-machine interaction.All the methods are connected in the scope of smart video surveillance framework. With the novel directions, three new datasets are built in this thesis as well. Enhancing the smart video surveillance system wit...

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Recognition in Terra Incognita

Beery

Horn

Perona

2018

Lecture Notes in Computer Science

475

402

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It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not yet available. We present a dataset designed to measure recognition generalization to novel environments. The images in our dataset are harvested from twenty camera traps deployed to monitor animal populations. Camera traps are fixed at one location, hence the background changes little across images; capture is triggered automatically, hence there is no human bias. The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available. In our experiments state-of-the-art algorithms show excellent performance when tested at the same location where they were trained. However, we find that generalization to new locations is poor, especially for classification systems. 1

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Animal recognition in the Mojave Desert: Vision tools for field biologists

Cited by 32 publications

References 13 publications

A Novel System for Non-Invasive Method of Animal Tracking and Classification in Designated Area Using Intelligent Camera System

A Novel System for Non-Invasive Method of Animal Tracking and Classification in Designated Area Using Intelligent Camera System

Exploring the frontier of smart video surveillance: novel domains and fine-grain event understanding

Recognition in Terra Incognita

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