Hey there,

Your hunch is spot-on—those tiny pixel brightness fluctuations between video frames from a Raspberry Pi camera (sensor noise, auto-exposure adjustments, subtle ambient lighting shifts) are definitely causing the bounding box jitter you're seeing, especially at 640x480 resolution where small pixel changes have a bigger relative impact on SSD-MobileNetV2's detections.

And no, that 21×21 Gaussian blur isn't the only solution—in fact, it's often not the optimal choice, since such a large kernel can blur critical object edges and even reduce detection accuracy, especially for smaller targets. Here are several better, more targeted approaches to fix this:

• 帧间平滑与跟踪约束：Since your video stream has continuous motion (objects don't jump 4-5 pixels randomly), you can use tracking to smooth out detection jitter. Maintain a cache of the last 3-5 detected bounding boxes and apply a weighted average (give more weight to recent frames) to the current frame's box. For even better results, implement a Kalman Filter: treat each detection as an observation, and use the filter to predict the object's next position/size—this will automatically suppress small, sudden fluctuations while preserving real object movement.

• 自适应局部亮度归一化：Instead of blurring the entire frame, fix brightness inconsistencies locally to preserve object details. Try using Contrast Limited Adaptive Histogram Equalization (CLAHE) with OpenCV:

  clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
  frame = clahe.apply(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY))
  frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
  

  This balances brightness across small tile regions without over-amplifying noise, unlike global histogram equalization. You can also try Local Contrast Normalization (LCN) for more precise pixel-level adjustment.

• 轻量图像平滑（替代大核高斯模糊）：If you still want to use blur, swap the 21×21 kernel for a much smaller one like 3×3 or 5×5 Gaussian blur. This will smooth out sensor noise without destroying object edges. For example:

  frame = cv2.GaussianBlur(frame, (5,5), 0)
  

• 摄像头参数与硬件优化：Tackle the problem at the source by stabilizing your camera's input. On Raspberry Pi, disable auto-exposure and auto-white balance using commands like raspistill -ex off -awb off (or set these parameters via OpenCV's VideoCapture API). Adding a small, constant LED补光灯 can also reduce ambient lighting fluctuations that cause pixel-level changes.

• 模型侧鲁棒性提升：If you're open to tweaking your detection pipeline, try a model with better inherent noise resistance, like YOLOv8n (nano version)—it's designed to handle small input variations better than SSD-MobileNetV2. You can also augment your training data with simulated sensor noise (brightness jitter, grain) to make your existing SSD model more robust to these fluctuations.

The motion detection approach you referenced uses frame differencing to spot changes, but here we're doing the opposite: suppressing irrelevant frame-to-frame changes to stabilize detections. The key is to choose a method that balances noise reduction with preserving the object details your detection model needs.

内容的提问来源于stack exchange，提问作者Vedanshu