TP-CanineNet: Temporal Context Contrastive Learning with Pseudo-Label Supervision for Abnormal Behavior Detection of Canine

Canines exhibit various behavioral abnormalities, such as excessive barking, destructive behaviors, and indoor defecation when left at home alone. Identifying these abnormal behaviors and implementing scientific and reasonable interventions can help improve canine welfare and promote harmonious coexistence between humans and companion animals. However, existing canine behavior recognition methods struggle to adapt to the characteristics of strong temporal continuity and uneven motion amplitude of abnormal behaviors exhibited by lonely dogs, resulting in inadequate temporal feature representation and low recognition accuracy. Therefore, this study developed a TP-CanineNet model based on a Weakly Supervised Video Anomaly Detection (WS-VAD) framework to address this issue. The model integrated a Temporal Context Aggregation (TCA) module to efficiently capture local–global temporal dependencies and suppress temporal noise, and further enhances the representation of temporal features in dog behaviors. Meanwhile, a Pseudo-Instance Discriminative Enhancement (PIDE) module is adopted to strengthen the feature distinction between abnormal and normal behaviors. We constructed an Alone-Dog dataset comprising 430 video samples and 60 ground-truth labeled samples to validate the model’s effectiveness. Experimental results showed that the proposed model achieved a frame-level AUC of 85.19% and an AP of 72.55%, representing improvements of 2.20% and 8.33%, respectively, over the baseline model. The method can provide intelligent detection of domestic dog behaviors when left alone at home.