diff --git a/recipes/people_line_counter/README.md b/recipes/people_line_counter/README.md
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+# Count Objects Crossing the Line
+
+![Demo Video](demo.gif)
+
+This example demonstrate the power of `trolo` using object detection to count object on a video, along with line zone counting functionality. It uses a pretained model for detection and provides an annotated output video with detected objects, tracking IDs, and counts of objects crossing a defined line zone. 
+
+## Features
+- **Object Detection**: Detect objects in video frames using a specified detection model.
+- **Object Tracking**: Tracks detected objects across video frames using the `ByteTrack` tracker from `supervision`
+- **Line Zone Counting**: Counts the number of objects crossing a predefined line in the video from `supervision`
+
+## Installation:
+
+-  Setup python environment and activate it [optional]
+	 ```shell
+	python3 -m venv venv
+	source venv/bin/activate
+	```	
+- Install required dependencies
+	```shell
+	pip install trolo supervision tqdm
+	```
+
+## How It Works
+1. The script loads a detection model using `DetectionPredictor` from `trolo`
+2. It reads video frames from the input video file.
+3. Each frame is processed to:
+   - Detect objects based on the specified confidence threshold.
+   - Track objects across frames.
+   - Annotate the frame with bounding boxes, labels, traces, and line zone information.
+4. The processed frames are either visualized in real-time or written to an output video file (if specified).
+
+## Command-Line Arguments
+
+| Argument          | Type     | Default     | Description                                                                 |
+|--------------------|----------|-------------|-----------------------------------------------------------------------------|
+| `--video_path`     | `str`    | (Required)  | Path to the input video file.                                               |
+| `--model_name`     | `str`    | `dfine-m`   | Name of the detection model to use.                                         |
+| `--output_path`    | `str`    | `None`      | Path to save the output annotated video.                                    |
+| `--vis`            | `bool`   | `True`      | Whether to visualize the annotated frames in real-time.                     |
+| `--conf_threshold` | `float`  | `0.35`      | Confidence threshold for filtering detections.                              |
+
+### Example Usage
+
+#### Basic Usage
+```bash
+python script.py --video_path people-walking.mp4
+```
+
+We thank Supervision to provide ready to use application apis.
diff --git a/recipes/people_line_counter/demo.gif b/recipes/people_line_counter/demo.gif
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diff --git a/recipes/people_line_counter/line_counter.py b/recipes/people_line_counter/line_counter.py
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+from typing import Optional
+import argparse
+
+from tqdm import tqdm
+import cv2
+import numpy as np
+import supervision as sv
+from trolo import DetectionPredictor, to_sv
+
+
+def detect_objects(model_predictor: DetectionPredictor,
+                   image: np.ndarray,
+                   conf_threshold: Optional[float]=0.35) -> sv.Detections:
+    """
+    Detect objects in an image using the provided model predictor.
+    """
+    image = sv.cv2_to_pillow(image)
+    results = model_predictor.predict([image], conf_threshold=conf_threshold)
+    detections = to_sv(results[0])
+    return detections
+
+
+def main(video_path: str,
+         model_name: Optional[str]="dfine-m" ,
+         output_path: Optional[str]=None,
+         vis: Optional[bool]=True,
+         conf_threshold: Optional[float]=0.35) -> int:
+    predictor = DetectionPredictor(model=model_name)
+    video_info = sv.VideoInfo.from_video_path(video_path)
+
+    # Change as per your requirement
+    color_lookup = sv.ColorLookup.TRACK
+    START = sv.Point(0, video_info.height // 2)
+    END = sv.Point(video_info.width, video_info.height // 2)
+
+    video_info.height = int(video_info.height * 0.5)
+    video_info.width = int(video_info.width * 0.5)
+
+    frames_generator = sv.get_video_frames_generator(video_path)
+    if output_path:
+        video_sink = sv.VideoSink(target_path=str(output_path), video_info=video_info).__enter__()
+
+    # Initialize the tracker and annotators
+    tracker = sv.ByteTrack()
+    box_annotator = sv.BoxAnnotator(color_lookup=color_lookup)
+    label_annotator = sv.LabelAnnotator(color_lookup=color_lookup)
+    track_annotator = sv.TraceAnnotator(color_lookup=color_lookup)
+
+    # Initialize the line zone counter and annotator
+    line_zone = sv.LineZone(start=START, end=END)
+    line_zone_annotator = sv.LineZoneAnnotator(
+        thickness=4,
+        text_thickness=4,
+        text_scale=2)
+    for frame in tqdm(frames_generator, desc="Counting Objects in the video"):
+        # Detect objects in the frame
+        detected_objects = detect_objects(model_predictor=predictor, image=frame, conf_threshold=conf_threshold)
+        # Update the tracker with the detected objects
+        tracked_detections = tracker.update_with_detections(detected_objects)
+        # Update the line zone counter
+        line_zone.trigger(tracked_detections)
+
+        # Annotate the frame
+        annotated_frame = frame.copy()
+        annotated_frame = box_annotator.annotate(annotated_frame, tracked_detections)
+        labels = [
+            f"{track_id[0]}"
+            for track_id in zip(tracked_detections.tracker_id)
+        ]
+        annotated_frame = label_annotator.annotate(annotated_frame, tracked_detections, labels)
+        annotated_frame = track_annotator.annotate(annotated_frame, tracked_detections)
+        annotated_frame = line_zone_annotator.annotate(frame=annotated_frame, line_counter=line_zone)
+
+        if vis:
+            cv2.imshow("Annotated Frame", annotated_frame)
+            key = cv2.waitKey(1)
+            if key == ord("q"):
+                break
+        if output_path:
+            annotated_frame = sv.resize_image(annotated_frame, (video_info.width, video_info.height))
+            annotated_frame = sv.pillow_to_cv2(annotated_frame)
+            video_sink.write_frame(annotated_frame)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Object detection and tracking in video with line zone counting.")
+    parser.add_argument("--video_path", type=str, required=True, help="Path to the input video file.")
+    parser.add_argument("--model_name", type=str, default="dfine-m", help="Name of the detection model.")
+    parser.add_argument("--output_path", type=str, default="demo.mp4", help="Path to save the output annotated video.")
+    parser.add_argument("--vis", type=bool, default=True,
+                        help="Whether to visualize the output frames (default: True).")
+    parser.add_argument("--conf_threshold", type=float, default=0.35,
+                        help="Confidence threshold for detection (default: 0.35).")
+
+    args = parser.parse_args()
+    main(
+        video_path=args.video_path,
+        model_name=args.model_name,
+        output_path=args.output_path,
+        vis=args.vis,
+        conf_threshold=float(args.conf_threshold)
+    )
diff --git a/recipes/people_line_counter/people-walking.mp4 b/recipes/people_line_counter/people-walking.mp4
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index 0000000..84bf38b
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diff --git a/trolo/__init__.py b/trolo/__init__.py
index 673faef..9679763 100644
--- a/trolo/__init__.py
+++ b/trolo/__init__.py
@@ -6,3 +6,5 @@
 from .configs import *
 
 from .loaders.registry import GLOBAL_CONFIG
+from .inference import DetectionPredictor
+from .utils.box_ops import to_sv
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