diff --git a/frigate/detectors/plugins/meta.py b/frigate/detectors/plugins/meta.py
index 9afd334ce..3b01d5fdf 100644
--- a/frigate/detectors/plugins/meta.py
+++ b/frigate/detectors/plugins/meta.py
@@ -6,6 +6,7 @@ from frigate.detectors.detector_config import BaseDetectorConfig, ModelConfig
 from frigate.util import deep_merge
 from typing import List, Tuple, Dict, Any, Literal
 from typing import Union
+from typing import Optional
 from typing_extensions import Annotated
 from enum import Enum
 from pydantic import Field, parse_obj_as
@@ -28,6 +29,10 @@ class MetaDetectorConfig(BaseDetectorConfig):
         default={"cpu": {"type": "cpu"}},
         title="Detector hardware configuration.",
     )
+    filtered_labels: Dict[str, Optional[List[str]]] = Field(
+        default={},
+        title="Labels to filter for each detector.",
+    )
     max_detections: int = Field(
         default=20,
         title="Maximum number of detections to return after merging results",
@@ -39,6 +44,8 @@ class MetaDetector(DetectionApi):
 
     def __init__(self, meta_detector_config: MetaDetectorConfig):
         self.max_detections = meta_detector_config.max_detections
+        self.filtered_labels = meta_detector_config.filtered_labels
+        self.labels = meta_detector_config.model.merged_labelmap
 
         self.detectors = []
 
@@ -62,12 +69,13 @@ class MetaDetector(DetectionApi):
                         "Customizing more than a detector model path is unsupported."
                     )
             merged_model = deep_merge(
-                detector_config.model.dict(exclude_unset=True),
                 meta_detector_config.model.dict(exclude_unset=True),
+                detector_config.model.dict(exclude_unset=True),
             )
             detector_config.model = ModelConfig.parse_obj(merged_model)
             meta_detector_config.detectors[key] = detector_config
             self.detectors.append(self.create_detector(detector_config))
+        self.meta_detector_config = meta_detector_config
 
     def merge_detections(self, detections_list: List[np.ndarray]) -> np.ndarray:
         all_detections = np.vstack(detections_list)
@@ -75,11 +83,28 @@ class MetaDetector(DetectionApi):
         return all_detections[sorted_indices[: self.max_detections]]
 
     def detect_raw(self, tensor_input) -> np.ndarray:
-        detections_list = [
-            detector.detect_raw(tensor_input) for detector in self.detectors
-        ]
+        detections_list = []
+        for i, detector in enumerate(self.detectors):
+            detector_key = list(self.meta_detector_config.detectors.keys())[i]
+            filtered_labels = self.filtered_labels.get(detector_key)
+            detections = detector.detect_raw(tensor_input)
+
+            if filtered_labels is not None:
+                detections = np.array(
+                    [
+                        d
+                        for d in detections
+                        if self.get_label_name(d[0]) in filtered_labels
+                    ]
+                )
+
+            detections_list.append(detections)
+
         return self.merge_detections(detections_list)
 
+    def get_label_name(self, index: int) -> str:
+        return self.labels.get(index)
+
     def create_detector(self, detector_config):
         current_module_name = os.path.splitext(os.path.basename(__file__))[0]
         modules_folder = os.path.dirname(os.path.abspath(__file__))