A Novel Approach to Detect Pedestrian from Still Images Using Random Subspace Method

Priya, V.; Rekha, P; Reshmi, K.C.; Kumar, Siva; Indhulekha, K.

doi:10.1016/j.protcy.2016.08.115

Cited by 2 publications

(1 citation statement)

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“…Applying a random subspace method (RSM)—a strategic learning method where the features of a target image are randomly sampled for ML training, has proven successful in managing partial human occlusions. Several studies have successfully detected humans within partially occluded still images, resulting in true-positive detection accuracies of 75.6% [ 44 , 58 ]. Such an application improves target detection performance within partially occluded images without compromising detection accuracy for non-occluded images [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning

Westworth

Chalmers

Fergus

et al. 2022

Sensors

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Using machine learning (ML) to automate camera trap (CT) image processing is advantageous for time-sensitive applications. However, little is currently known about the factors influencing such processing. Here, we evaluate the influence of occlusion, distance, vegetation type, size class, height, subject orientation towards the CT, species, time-of-day, colour, and analyst performance on wildlife/human detection and classification in CT images from western Tanzania. Additionally, we compared the detection and classification performance of analyst and ML approaches. We obtained wildlife data through pre-existing CT images and human data using voluntary participants for CT experiments. We evaluated the analyst and ML approaches at the detection and classification level. Factors such as distance and occlusion, coupled with increased vegetation density, present the most significant effect on DP and CC. Overall, the results indicate a significantly higher detection probability (DP), 81.1%, and correct classification (CC) of 76.6% for the analyst approach when compared to ML which detected 41.1% and classified 47.5% of wildlife within CT images. However, both methods presented similar probabilities for daylight CT images, 69.4% (ML) and 71.8% (analysts), and dusk CT images, 17.6% (ML) and 16.2% (analysts), when detecting humans. Given that users carefully follow provided recommendations, we expect DP and CC to increase. In turn, the ML approach to CT image processing would be an excellent provision to support time-sensitive threat monitoring for biodiversity conservation.

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Section: Discussionmentioning

confidence: 99%