The challenge will explore the use of CV techniques for endangered wildlife conservation, specifically focusing on the Amur tiger, also known as Siberian tiger or the Northeast-China tiger. The Amur tiger population is concentrated in the Far East, particularly the Russian Far East and Northeast China. The remaining wild population is estimated to be 600 individuals, so conservation is of crucial importance.

Dataset: With the help of WWF, a third-party company (MakerCollider) collected more than 8,000 Amur tiger video clips of 92 individuals from ~10 zoos in China. This raw dataset is donated to us for academic usage. We organize efforts to make bounding-box, keypoint-based pose, and identity annotations for sampled video frames and formuate the ATRW (Amur Tiger Re-identification in the Wild) dataset. Figure 1 illustrates some example bounding box and pose keypoint annotations in our ATRW dataset. Our dataset is the largest wildlife re-ID dataset to date, Table 1 lists a comparison of current wildlife re-ID datasets. The dataset will be divided into training, validation, and testing subsets. The training/validation subsets along with annotations will be released to public, with the annotations for the test subset withheld by the organizers.

Figure 1. Illustration of bounding box and pose keypoint annotations in our ATRW dataset.

Datasets ATRW [1,2] C-Zoo[3] C-Tai[3] TELP[4] α-whale[5]
Target Tiger Tiger Chimpanzees Chimpanzees Elephant Whale
Wild × × ×
Pose annotation × ×× × ×
#Images or #Clips 8,076* 278 2,109 5,078 2,078 924
#BBoxes 9,496 278 2,109 5,078 2,078 924
#BBoxes with ID 3,649 278 2,109 5,078 2,078 924
#identities 92 278 24 78 276 38
#BBoxes/ID 39.7 1 19.9 9.7 20.5 24.3
Table 1. Comaprison of current wildlife re-ID datasets.

Requirement: We require that participants agree to open-source their solution to support wildlife conservation. All participating teams are required to use only our dataset for training, and submit their challenge results as well as full source-code packages for evaluation.


Tiger Detection: From images/videos captured by cameras, this task aims to place tight bounding boxes around tigers. As the detection may run on the edge (smart cameras), both the detection accuracy (in terms of AP) and the runtime cost are used to measure the quality of the detector.

Tiger Pose Detection: From images/videos with detected tiger bounding boxes, this task aims to estimate tiger pose (i.e., keypoint landmarks) for tiger image alignment/normalization, so that pose variations are removed or alleviated in the tiger re-identification step. We will use mean average precision (mAP) and object keypoint similarity (OKS) to evaluate submissions.

Tiger Re-ID with Human Alignment (Plain Re-ID): We define a set of queries and a target database of Amur tigers. Both queries and targets in the database are already annotated with bounding boxes and pose information. Tiger re-identification aims to find all the database images containing the same tiger as the query. Both mAP and rank-1 accuracy will be used to evaluate accuracy.

Tiger Re-ID in the Wild: This track will evaluate the accuracy of tiger re-identification in wild with a fully automated pipeline. To simulate the real use case, no annotations are provided. Submissions should automatically detect and identify tigers in all images in the test set. Both mAP and rank-1 accuracy will be used to evaluate the accuracy of different models.


The workshop will provide awards for each challenge track winner team thanks to our sponsor's generous donation. Detailed award info will be available soon.


Evaluation server and detailed protocol will be available soon.

Important Dates

  • Training data release: June 28, 2019
  • Testing data release: July 26, 2019
  • Result submission deadline: August 2, 2019
  • Result notification: August 9, 2019
  • Challenge paper submission: August 15, 2019
  • Acceptance notification: August 23, 2019
  • Camera Ready: August 30, 2019
  • Workshop: October 27, 2019


  1. K Ullas Karanth, James D Nichols, N Samba Kumar, et al. Tigers and their prey: predicting carnivore densities from prey abundance. PNAS 101, 14 (2004),4854–4858.
  2. K Ullas Karanth, James D Nichols, N Samba Kumar, and James E Hines. Assessing tiger population dynamics using photographic capture–recapture sampling. Ecology 87, 11 (2006), 2925–2937.
  3. Alexander Freytag, Erik Rodner, Marcel Simon, Alexander Loos, et al. Chimpanzee faces in the wild: Log-euclidean cnns for predicting identities and attributes of primates. In German Conference on Pattern Recognition, 2016.
  4. Matthias Körschens, Björn Barz, and Joachim Denzler. Towards automatic identification of elephants in the wild. arXiv preprint arXiv:1812.04418(2018).
  5. Andrei Polzounov, Ilmira Terpugova, Deividas Skiparis, and Andrei Mihai. Right whale recognition using convolutional neural networks. arXiv preprint arXiv:1604.05605(2016).