Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection

Beery, Sara; Wu, Guanhang; Rathod, Vivek; Votel, Ronny; Huang, Jonathan

doi:10.1109/cvpr42600.2020.01309

Cited by 113 publications

(81 citation statements)

References 35 publications

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“…Since it is suitable to any species, further data analysis on other species will help answer this question. However, additional strategies would help including the integration of contextual information (Beery et al ., 2019; Terry et al ., 2020) such as time, GPS positioning or animal social context. Using accurate segmentation of animal body (He et al ., 2017; Brodrick et al ., 2019) will undoubtedly be a solution against side effects of rectangular cropping.…”

Section: Discussionmentioning

confidence: 99%

Revisiting animal photo-identification using deep metric learning and network analysis

Miele

Dussert

Spataro

et al. 2020

Preprint

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An increasing number of research programs rely on photographic capture-recapture (vs. direct marking) of individuals to study distribution and demography within animal populations. Photoidentification of individuals living in the wild is sometimes feasible using idiosyncratic coat or skin patterns, like for giraffes. When performed manually, the task is tedious and becomes almost impossible as populations grow in size. Computer vision techniques are an appealing and unavoidable help to tackle this apparently simple task in the big-data era. In this context, we propose to revisit giraffe re-identification using convolutional neural networks (CNNs).We first developed an end-to-end pipeline to retrieve a comprehensive set of re-identified giraffes from about 4, 000 raw photographs. To do so, we combined CNN-based object detection, SIFT pattern matching, and image similarity networks. We then quantified the performance of deep metric learning to retrieve the identity of known and unknown individuals. The re-identification performance of CNNs reached a top 5 accuracy of about 90%. Fully based on open-source software packages, our work paves the way for further attempts to build CNN-based pipelines for re-identification of individual animals, in giraffes but also in other species.

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Section: Discussionmentioning

confidence: 99%

Revisiting animal photo-identification using deep metric learning and network analysis

Miele

Dussert

Spataro

et al. 2020

Preprint

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“…We can aggregate information from remote sensing, passive and active monitoring sensors, ecological samples, and the natural history record to paint a cohesive picture of global biodiversity and help fight to protect it. species identification by letting the model know which species are most likely to be seen in a given area at a specific time [8], and building models that can share information across data collected by a given static sensor, helping the model adapt to previously unseen environments [9]. Aggregating data allows researchers to share the cost and scale up, in collection effort, data processing effort, and across jurisdictions.…”

Section: Biodiversity Data Poses New Challenges For Machine Learningmentioning

confidence: 99%

Scaling Biodiversity Monitoring for the Data Age

Beery

2021

XRDS

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“…We remark that it was only an example of a DNN-based approach to build the system, and that alternative approaches could be proposed. For instance, considering that the cameras are stationary, temporal context information could be used (Beery et al, 2020), to reduce the number of false positives. Furthermore, binary content-based image classifiers could be applied to areas of interest to classify whether TTL cabin readiness is complied or not.…”

Section: Preliminary Tests With Pre-trained Generalist Dnn Modelsmentioning

confidence: 99%

Building a Camera-based Smart Sensing System for Digitalized On-demand Aircraft Cabin Readiness Verification

Unzueta

García²,

García

et al. 2020

Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems

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Currently, aircraft cabin operations such as the verification of taxi, take-off, and landing (TTL) cabin readiness are done manually. This results in an increased workload for the crew, operational inefficiencies, and a non-negligible risk of human errors in handling safety-related procedures. For TTL, specific cabin readiness requirements apply to the passenger, to the position of seat components and cabin luggage. The usage of cameras and vision-based object-recognition algorithms may offer a promising solution for specific functionalities such as cabin luggage detection. However, building a suitable camera-based smart sensing system for this purpose brings many challenges as it needs to be low weight, with competitive cost and robust recognition capabilities on individual seat level, complying with stringent constraints related to airworthiness certification. This position paper analyzes and discusses the main technological factors that system designers should consider for building such an intelligent system. These include the sensor setup, system training, the selection of appropriate camera sensors and lenses, AI-processors, and software tools for optimal image acquisition and image content analysis with Deep Neural Network (DNN)-based recognition methods. Preliminary tests with pre-trained generalist DNN-based object detection models are also analyzed to assist with the training and deployment of the recognition methods.

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Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection

Cited by 113 publications

References 35 publications

Revisiting animal photo-identification using deep metric learning and network analysis

Revisiting animal photo-identification using deep metric learning and network analysis

Scaling Biodiversity Monitoring for the Data Age

Building a Camera-based Smart Sensing System for Digitalized On-demand Aircraft Cabin Readiness Verification

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