Detecting Kangaroos in the Wild: The First Step towards Automated Animal Surveillance




Recent studies in computer vision have provided new solutions to real-world problems in field works. In this paper, we focus on using computer vision methods to assist with the study of kangaroos in the wild. Yet there is no kangaroo related dataset for researchers of interest to use. So we proposed a kangaroo dataset for the kangaroo related research. Unlike most object detection tasks, kangaroo detection in the wild possesses markedly more difficult challenges due to the complex background, varying illumination, occlusion and the multiple poses and extremely deformable body.


Proposed Method


Although kangaroos could drastically twist their body, we observed that the state-of-the-art DPM method can handle the challenges. DPM is a star model consists of a root filter and a set of part filters. The root filter is a coarse representation of the object and the part filter is to capture the parts of the objects. The parts can have certain level of deformation around the root filter. DPM can fail to detect kangaroos due to some parts are missing caused by occlusion and poses, yet the score of these kind of images are still higher than the true negatives (i.e. the background). We shall call these as weak negatives (denoted which also include false positives). In DPM, images from  will result in a high FAR. 


We propose to use pose-specific SVMs to re-score the images in. The key idea is to train a pose-specific SVM to deal with the occluded pattern caused by a certain pose. The multi-pose SVMs serve as an additional component in the DPM framework. After training of DPM and multi-pose SVMs (separately), we combine the score from DPM and the multi-pose SVM as follows in testing process.



Where  is our final detector score. is the highest SVM score among eight pose SVMs (front, rear, left, right, front-left, front-right, rear-left and rear-right). We further define two threshold T1>T2 from cross validation. Only images whose DPM score falls between T1 and T2 will be put into.


Experiments and Results


As we can see from the table, the proposed approach achieves marked improvement over standard DPM. The challenge posed by the dataset is also reflected in the performance of HOG+SVM method.


Tab.1 Detection result on Kangaroos.

In the second evaluation, we used our proposed approach in a Kangaroo population study. We collected 4,000 frames from four different locations to get the accumulated kangaroo population distribution over the 24 hours. From this result, we found that kangaroos tend to be more active in the early evening in these locations and is much more active in the night than day time. This finding corroborates previous biological studies suggesting that most kangaroos are nocturnal animals.


Fig.1 Kangaroo activity pattern





Teng Zhang, Arnold Wiliem, Graham Hemson and Brian C. Lovell, Detecting kangaroos in the wild: the first step towards automated animal surveillance, ICASSP, Brisbane, Australia, Apr 2015.




Our data were collected in several national parks across Queensland State during 2013. In the initial stage, 1,900 cropped images are selected from the raw frames which include other animals such as dingo, wild cat and emu. You can download the dataset from here: