4 research outputs found
Automatic Detection and Recognition of Individuals in Patterned Species
Visual animal biometrics is rapidly gaining popularity as it enables a
non-invasive and cost-effective approach for wildlife monitoring applications.
Widespread usage of camera traps has led to large volumes of collected images,
making manual processing of visual content hard to manage. In this work, we
develop a framework for automatic detection and recognition of individuals in
different patterned species like tigers, zebras and jaguars. Most existing
systems primarily rely on manual input for localizing the animal, which does
not scale well to large datasets. In order to automate the detection process
while retaining robustness to blur, partial occlusion, illumination and pose
variations, we use the recently proposed Faster-RCNN object detection framework
to efficiently detect animals in images. We further extract features from
AlexNet of the animal's flank and train a logistic regression (or Linear SVM)
classifier to recognize the individuals. We primarily test and evaluate our
framework on a camera trap tiger image dataset that contains images that vary
in overall image quality, animal pose, scale and lighting. We also evaluate our
recognition system on zebra and jaguar images to show generalization to other
patterned species. Our framework gives perfect detection results in camera
trapped tiger images and a similar or better individual recognition performance
when compared with state-of-the-art recognition techniques.Comment: 12 pages, ECML-PKDD 201
Semi-Supervised Clustering with Neural Networks
Clustering using neural networks has recently demonstrated promising
performance in machine learning and computer vision applications. However, the
performance of current approaches is limited either by unsupervised learning or
their dependence on large set of labeled data samples. In this paper, we
propose ClusterNet that uses pairwise semantic constraints from very few
labeled data samples (<5% of total data) and exploits the abundant unlabeled
data to drive the clustering approach. We define a new loss function that uses
pairwise semantic similarity between objects combined with constrained k-means
clustering to efficiently utilize both labeled and unlabeled data in the same
framework. The proposed network uses convolution autoencoder to learn a latent
representation that groups data into k specified clusters, while also learning
the cluster centers simultaneously. We evaluate and compare the performance of
ClusterNet on several datasets and state of the art deep clustering approaches.Comment: 9 Page
Unique Identification of Macaques for Population Monitoring and Control
Despite loss of natural habitat due to development and urbanization, certain
species like the Rhesus macaque have adapted well to the urban environment.
With abundant food and no predators, macaque populations have increased
substantially in urban areas, leading to frequent conflicts with humans.
Overpopulated areas often witness macaques raiding crops, feeding on bird and
snake eggs as well as destruction of nests, thus adversely affecting other
species in the ecosystem. In order to mitigate these adverse effects,
sterilization has emerged as a humane and effective way of population control
of macaques. As sterilization requires physical capture of individuals or
groups, their unique identification is integral to such control measures. In
this work, we propose the Macaque Face Identification (MFID), an image based,
non-invasive tool that relies on macaque facial recognition to identify
individuals, and can be used to verify if they are sterilized. Our primary
contribution is a robust facial recognition and verification module designed
for Rhesus macaques, but extensible to other non-human primate species. We
evaluate the performance of MFID on a dataset of 93 monkeys under closed set,
open set and verification evaluation protocols. Finally, we also report state
of the art results when evaluating our proposed model on endangered primate
species
Primate Face Identification in the Wild
Ecological imbalance owing to rapid urbanization and deforestation has
adversely affected the population of several wild animals. This loss of habitat
has skewed the population of several non-human primate species like chimpanzees
and macaques and has constrained them to co-exist in close proximity of human
settlements, often leading to human-wildlife conflicts while competing for
resources. For effective wildlife conservation and conflict management, regular
monitoring of population and of conflicted regions is necessary. However,
existing approaches like field visits for data collection and manual analysis
by experts is resource intensive, tedious and time consuming, thus
necessitating an automated, non-invasive, more efficient alternative like image
based facial recognition. The challenge in individual identification arises due
to unrelated factors like pose, lighting variations and occlusions due to the
uncontrolled environments, that is further exacerbated by limited training
data. Inspired by human perception, we propose to learn representations that
are robust to such nuisance factors and capture the notion of similarity over
the individual identity sub-manifolds. The proposed approach, Primate Face
Identification (PFID), achieves this by training the network to distinguish
between positive and negative pairs of images. The PFID loss augments the
standard cross entropy loss with a pairwise loss to learn more discriminative
and generalizable features, thus making it appropriate for other related
identification tasks like open-set, closed set and verification. We report
state-of-the-art accuracy on facial recognition of two primate species, rhesus
macaques and chimpanzees under the four protocols of classification,
verification, closed-set identification and open-set recognition.Comment: arXiv admin note: text overlap with arXiv:1811.0074