Automatic Individual Pig Detection and Tracking in Pig Farms

Abstract

Individual pig detection and tracking is an important requirement in many video-based pig monitoring applications. However, it still remains a challenging task in complex scenes, due to problems of light fluctuation, similar appearances of pigs, shape deformations, and occlusions. In order to tackle these problems, we propose a robust on-line multiple pig detection and tracking method which does not require manual marking or physical identification of the pigs and works under both daylight and infrared (nighttime) light conditions. Our method couples a CNN-based detector and a correlation filter-based tracker via a novel hierarchical data association algorithm. In our method, the detector gains the best accuracy/speed trade-off by using the features derived from multiple layers at different scales in a one-stage prediction network. We define a tag-box for each pig as the tracking target, from which features with a more local scope are extracted for learning, and the multiple object tracking is conducted in a key-points tracking manner using learned correlation filters. Under challenging conditions, the tracking failures are modelled based on the relations between responses of the detector and tracker, and the data association algorithm allows the detection hypotheses to be refined; meanwhile the drifted tracks can be corrected by probing the tracking failures followed by the re-initialization of tracking. As a result, the optimal tracklets can sequentially grow with on-line refined detections, and tracking fragments are correctly integrated into respective tracks while keeping the original identifications. Experiments with a dataset captured from a commercial farm show that our method can robustly detect and track multiple pigs under challenging conditions. The promising performance of the proposed method also demonstrates the feasibility of long-term individual pig tracking in a complex environment and thus promises commercial potential.

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

“…Low-level technologies consist of tagging individual animals with unique markers, such as chalk, paint, markers, or wax markings ( de Moraes Weber et al, 2020 ). More advanced technologies include face detection ( Yao et al, 2019 ), muzzle detection ( Noviyanto and Arymurthy, 2013 ; Tharwat et al, 2014 ), automatic detection via ML, and DL approaches applied to surveillance videos ( Zhang et al, 2018a ) or hybrid systems that combine radio-frequency identification sensors and CV technologies ( Velez et al, 2013 ). A review of cattle detection methods is presented by Awad (2016) .…”

EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations

“…Hence, the primary purpose of solving this issue would be identification of the number of pigs and mortality prevention using early detection of abnormalities. For previous research on identification, References [11,16-18, 20-22,24-26,29,31,38,44-48] for detection and References [10,19,37,39,45] for tracking exist. In addition, previous studies for early detection of abnormalities exist as various topics, including research on the movement of pigs [17,62], research on aggressive behavior of pigs [63,64], research on attitude change [16,22,23,31,32,34,35,40,46], research on mounting behavior [21], research on low-growth pig's behavior [49], research on pig weight [29,33,38] and research on the density of pigs [9,11].…”

Long-Term Tracking of Group-Housed Livestock Using Keypoint Detection and MAP Estimation for Individual Animal Identification

“…As a result that they must be physically attached to the animals, these are invasive devices that impact animal welfare. Furthermore, due to financial constraints and concerns about durability and hardware management [ 25 ], modern approaches to precision livestock farming are trending toward non-invasive, vision-based solutions [ 26 , 27 , 28 , 29 ].…”

“…Zhang et al. [ 29 ] proposed a method to detect pigs and associate them across frames using a combination of trainable methods. Detection is based on the architecture of the Single-Shot Detector (SSD) [ 66 ] and it is used to identify pigs via a location near the middle of their backs, which they refer to as “tag-boxes.” To associate detections between frames, they apply a trainable correlation filter to the tag-box regions to track pigs as a single feature point in the images.…”