Deep convolutional neural network based species recognition for wild animal monitoring

Chen, Guobin; Han, Tony Xiao; He, Zhihai; Kays, Roland; Forrester, Tavis

doi:10.1109/icip.2014.7025172

Cited by 148 publications

(113 citation statements)

References 16 publications

Supporting

Mentioning

109

Contrasting

Unclassified

Order By: Relevance

“…A deep convolutional neural network for species recognition in wild nature on camera-trap data was published in [6]. Their dataset was captured with motion triggered camera trap and included 20 animal species.…”

Section: Related Workmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

Section: Related Workmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Perhaps the work that most similar to us is Chen et al [18] in which they monitor several wild animals in North America national parks. The main difference is that the target animals and the environment are different (Australia compares to the US) which lead to a different strategy and dataset.…”

Section: Video Surveillance In Novel Domainsmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

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

Zhang¹

View full text Add to dashboard Cite

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...

show abstract

Deep learning object detection to estimate the nectar sugar mass of flowering vegetation

Hicks

Baude

Kratz

et al. 2021

Ecol Sol and Evidence

View full text Add to dashboard Cite

Floral resources are a key driver of pollinator abundance and diversity, yet their quantification in the field and laboratory is laborious and requires specialist skills. Using a dataset of 25,000 labelled tags of fieldwork‐realistic quality, a convolutional neural network (Faster R‐CNN) was trained to detect the nectar‐producing floral units of 25 taxa in surveyors’ quadrat images of native, weed‐rich grassland in the United Kingdom. Floral unit detection on a test set of 50 model‐unseen images of comparable vegetation returned a precision of 90%, recall of 86% and F1 score (the harmonic mean of precision and recall) of 88%. Model performance was consistent across the range of floral abundance in this habitat. Comparison of the nectar sugar mass estimates made by the CNN and three human surveyors returned similar means and standard deviations. Over half of the nectar sugar mass estimates made by the model fell within the absolute range of those of the human surveyors. The optimal number of quadrat image samples was determined to be the same for the CNN as for the average human surveyor. For a standard quadrat sampling protocol of 10–15 replicates, this application of deep learning could cut pollinator‐plant survey time per stand of vegetation from hours to minutes. The CNN is restricted to a single view of a quadrat, with no scope for manual examination or specimen collection, though in contrast to human surveyors its object detection is deterministic and its floral unit definition is standardized. As agri‐environment schemes move from prescriptive to results‐based, this approach provides an independent barometer of grassland management which is usable by both landowner and scheme administrator. The model can be adapted to visual estimations of other ecological resources such as winter bird food, floral pollen volume, insect infestation and tree flowering/fruiting, and by adjustment of classification threshold may show acceptable taxonomic differentiation for presence–absence surveys.

show abstract

Deep convolutional neural network based species recognition for wild animal monitoring

Cited by 148 publications

References 16 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

Deep learning object detection to estimate the nectar sugar mass of flowering vegetation

Contact Info

Product

Resources

About