Does the tail show when the nose knows? AI-enhanced analysis of tail kinematics outperforms human experts at predicting when detection dogs find their target odor

Detection dogs are utilized for searching and alerting various substances
due to their olfactory abilities. Dog trainers report being able to
"predict" such identification based on subtle behavioral
changes, such as tail movement. This study investigated tail kinematic
patterns of dogs during a detection task, using computer vision to detect
tail movement. Eight dogs searched for a target odor on a search wall,
alerting to its presence by standing still. Dogs’ detection accuracy,
against a distractor odor, was 100% with trained concentration while,
during threshold assessment, progressively reached 50%. In the target odor
area dogs exhibited a higher left-sided tail-wagging amplitude. The AI
model showed a 77% accuracy score in the classification and, in line with
the dogs’ performance, progressively decreased at lower odour
concentrations. Additionally, we compared the performance of an AI
classification model to that of 190 detection dog handlers in determining
when a dog was in the vicinity of a target odor.  The AI model
outperformed dog professionals, correctly classifying 66% against 46% of
videos. These findings indicate the potential of AI-enhanced techniques to
reveal new insights into dogs’ behavioral repertoire during odour
discrimination.

嗅探犬（Detection dogs）凭借其优异的嗅觉感知能力，被广泛应用于各类物质的搜寻与预警作业。训犬师称，他们能够通过犬只细微的行为变化（如尾部动作）预判其识别目标的结果。本研究借助计算机视觉技术捕捉犬只尾部动作，对其在嗅探任务中的尾部运动学模式展开了分析。实验中，8只训练犬在搜寻墙面上搜寻目标气味，并通过静止站立的动作完成气味存在的报警。在标准训练浓度下，犬只对干扰气味的识别准确率可达100%；而在阈值测试阶段，其准确率逐步降至50%。在目标气味区域内，犬只的左侧摇尾幅度显著更高。本研究构建的AI分类模型准确率达77%，且与犬只的表现一致，在气味浓度降低时分类准确率逐步下降。此外，本研究还将该AI分类模型的表现与190名嗅探犬训导员的表现进行了对比，对比任务为判断犬只是否处于目标气味的附近区域。AI模型的表现优于专业训导人员：其对视频样本的正确分类率达66%，而专业人员的正确分类率仅为46%。上述研究结果表明，结合AI增强技术，有望为揭示犬只在气味辨别过程中的行为模式提供全新视角。